Multiple access using orthogonal time frequency space modulation

ABSTRACT

An Orthogonal Time Frequency Space Modulation (OTFS) modulation scheme achieving multiple access by multiplexing multiple signals at the transmitter-side performs allocation of transmission resources to a first signal and a second signal, combining and converting to a transmission format via OTFS modulation and transmitting the signal over a communication channel. At the receiver, multiplexed signals are recovered using orthogonality property of the basis functions used for the multiplexing at the transmitter.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This patent document is continuation of U.S. patent application Ser. No.15/758,322, filed on Mar. 7, 2018, which is a 35 U.S.C. § 371 NationalStage application of International Application No. PCT/US2016/050579entitled “MULTIPLE ACCESS USING ORTHOGONAL TIME FREQUENCY SPACEMODULATION”, filed on Sep. 7, 2016, which claims the benefit of priorityfrom U.S. Provisional Patent Application 62/215,127, entitled“ORTHOGONAL TIME FREQUENCY SPACE A Novel Modulation Technique Addressingthe Challenges of 5G Networks,” filed on Sep. 7, 2015. This applicationalso claims the benefit of priority from U.S. Provisional PatentApplication 62/215,219, entitled “OTFS Compatibility with LTE,” filed onSep. 8, 2015. All of the aforementioned patent applications areincorporated by reference herein in their entirety.

TECHNICAL FIELD

This document relates to the field of telecommunications, in particular,estimation and compensation of impairments in telecommunications datachannels.

BACKGROUND

Due to an explosive growth in the number of wireless user devices andthe amount of wireless data that these devices can generate or consume,current wireless communication networks are fast running out ofbandwidth to accommodate such a high growth in data traffic and providehigh quality of service to users.

Various efforts are underway in the telecommunication industry to comeup with next generation of wireless technologies that can keep up withthe demand on performance of wireless devices and networks.

SUMMARY

Techniques for transmission and reception of signals using OTFSmodulation techniques are disclosed. Signal multiplexing may be achievedby assigning non-overlapping time-frequency and/or delay-Dopplerresources.

In one example aspect, a signal transmission technique is disclosed. Thetechnique includes performing a logical mapping of transmissionresources of the digital communication channel along a firsttwo-dimensional resource plane represented by a first and a secondorthogonal axes corresponding to a first transmission dimension and asecond transmission dimension respectively, allocating, to a firstsignal, a first group of transmission resources from the logical mappingfor transmission, allocating, to a second signal, a second group ofresources from the logical mapping for transmission, transforming, usinga first two-dimensional transform, a combination of the first signalhaving the first group of transmission resources and the second signalhaving the second group of transmission resources to a correspondingtransformed signal in a second two-dimensional resource planerepresented by a third and a fourth orthogonal axes corresponding to athird transmission dimension and a fourth transmission dimensionrespectively, converting the transformed signal to a formatted signalaccording to a transmission format of the communications channel, andtransmitting the formatted signal over the communications channel.

In another example aspect, a signal reception method is disclosed. Themethod includes receiving a signal transmission comprising at least twocomponent signals multiplexed together, transforming, using anorthogonal transform, the signal transmission into a post-processingformat, wherein the post-processing format represents the at least twocomponent signals in a two-dimensional time-frequency plane, recovering,by performing an orthogonal time frequency space transformation, amultiplexed signal in a two-dimensional delay-Doppler plane, from thepost-processing format, and demultiplexing the multiplexed signal torecover one of the at least two component signals.

In yet another example aspect, a signal transmission method isdisclosed. The signal transmission method includes performing a logicalmapping of transmission resources of the digital communication channelalong a first two-dimensional resource plane represented by a first anda second orthogonal axes corresponding to a first transmission dimensionand a second transmission dimension respectively, allocating, to a firstsignal, a first group of transmission resources from the logical mappingfor transmission, transforming, using a first two-dimensional transform,the first signal having the first group of transmission resources to acorresponding transformed signal in a second two-dimensional resourceplane represented by a third and a fourth orthogonal axes correspondingto a third transmission dimension and a fourth transmission dimensionrespectively, converting the transformed signal to a formatted signalaccording to a transmission format of the communications channel, andtransmitting the formatted signal over the communications channel.

These, and other aspects are described in the present document.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example trajectory of Time Varying Impulse Response forAccelerating Reflector.

FIG. 2 shows an example Delay-Doppler Representation for AcceleratingReflector Channel.

FIG. 3 depicts example levels of Abstraction: Signaling over the (i)actual channel with a signaling waveform (ii) the time-frequency Domain(iii) the delay-Doppler Domain.

FIG. 4 illustrates notation used to Denote Signals at Various Stages ofTransmitter and Receiver.

FIG. 5 illustrates a conceptual Implementation of the HeisenbergTransform in the Transmitter and the Wigner Transform in the Receiver.

FIG. 6 illustrates cross-correlation between g_(tr)(t) and g_(r)(t) forOFDM Systems.

FIG. 7 depicts example of information symbols in the Information(Delay-Doppler) Domain (Right), and Corresponding Basis Functions in theTime-Frequency Domain (Left).

FIG. 8 illustrates an example one Dimensional Multipath Channel Example:(i) Sampled Frequency Response at Δf=1 Hz (ii) Periodic FourierTransform with Period 1/Δf=1 sec (iii) Sampled Fourier Transform withPeriod 1/Δf and Resolution 1/MΔf.

FIG. 9 illustrates a one Dimensional Doppler Channel Example: (i)Sampled Frequency Response at T_(s)=1 sec (ii) Periodic FourierTransform with Period 1/T_(s)=1 Hz (iii) Sampled Fourier Transform withPeriod 1/T_(s) and Resolution 1/NT_(s).

FIG. 10 depicts an example of a time-Varying Channel Response in theTime-Frequency Domain.

FIG. 11 shows an example of a SDFT of Channel response—(τ,v)Delay-Doppler Domain.

FIG. 12 shows an example SFFT of Channel Response—Sampled (τ,v)Delay-Doppler Domain.

FIG. 13 shows an example transformation of the Time-Frequency Plane tothe Doppler-Delay Plane.

FIG. 14 shows an example of a Discrete Impulse in the orthogonal timefrequency space (OTFS) Domain Used for Channel Estimation.

FIG. 15 shows examples of different Basis Functions, assigned todifferent users, span the whole Time-Frequency Frame.

FIG. 16 an example of multiplexing three users in the Time-FrequencyDomain

FIG. 17 shows an example of multiplexing three users in theTime-Frequency Domain with Interleaving.

FIG. 18 shows an example of OTFS Architecture.

FIG. 19 is a graphical representation of an OTFS delay-Dopplertransform.

FIG. 20 is a graphical representation of an OTFS delay-Dopplertransform.

FIG. 21 shows a multiplexing scheme.

FIG. 22 shows a multiplexing scheme.

FIG. 23 shows a multiplexing scheme.

FIG. 24 shows a multiplexing scheme.

FIG. 25 shows a multiplexing scheme.

FIG. 26 shows a multiplexing scheme.

FIG. 27 shows a multiplexing scheme.

FIG. 28 is another example of an OTFS architecture.

FIG. 29 is a flowchart representation of a method of signaltransmission.

FIG. 30 is a flowchart representation of a method of signal reception.

FIG. 31 is a block diagram of an example communication apparatus.

DETAILED DESCRIPTION

Section headings are used in this document to help improve readabilityand do not limit scope of the technology discussed in each section onlyto that section. Furthermore, for ease of explanation, a number ofsimplifying assumptions have been made. Although these simplifyingassumptions are intended to help convey ideas, they are not intended tobe limiting.

4G wireless networks have served the public well, providing ubiquitousaccess to the internet and enabling the explosion of mobile apps,smartphones and sophisticated data intensive applications like mobilevideo. This continues an honorable tradition in the evolution ofcellular technologies, where each new generation brings enormousbenefits to the public, enabling astonishing gains in productivity,convenience, and quality of life.

Looking ahead to the demands that the ever increasing and diverse datausage is putting on the network, it is becoming clear to the industrythat current 4G networks will not be able to support the foreseen needsin the near term future. The data traffic volume has been and continuesto increase exponentially. AT&T reports that its network has seen anincrease in data traffic of 100,000% in the period 2007-2015. Lookinginto the future, new applications like immersive reality, and remoterobotic operation (tactile internet) as well as the expansion of mobilevideo are expected to overwhelm the carrying capacity of currentsystems. One of the goals of 5G system design is to be able toeconomically scale the network to 750 Gbps per sq·Km in dense urbansettings, something that is not possible with today's technology.

Beyond the sheer volume of data, the quality of data delivery will needto improve in next generation systems. The public has become accustomedto the ubiquity of wireless networks and is demanding a wirelineexperience when untethered. This translates to a requirement of 50+ Mbpseverywhere (at the cell edge), which will require advanced interferencemitigation technologies to be achieved.

Another aspect of the quality of user experience is mobility. Currentsystems' throughput is dramatically reduced with increased mobile speedsdue to Doppler effects which evaporate MIMO capacity gains. Future 5Gsystems aim to not only increase supported speeds up to 500 Km/h forhigh speed trains and aviation, but also support a host of newautomotive applications for vehicle-to-vehicle andvehicle-to-infrastructure communications.

While the support of increased and higher quality data traffic isnecessary for the network to continue supporting the user needs,carriers are also exploring new applications that will enable newrevenues and innovative use cases. The example of automotive and smartinfrastructure applications discussed above is one of several. Othersinclude the deployment of public safety ultra-reliable networks, the useof cellular networks to support the sunset of the PSTN, etc. The biggestrevenue opportunity however, is arguably the deployment of large numberof internet connected devices, also known as the internet of things(IoT). Current networks however are not designed to support a very largenumber of connected devices with very low traffic per device.

In summary, current LTE networks cannot achieve the cost/performancetargets required to support the above objectives, necessitating a newgeneration of networks involving advanced PHY technologies. There arenumerous technical challenges that will have to be overcome in 5Gnetworks as discussed next.

4G Technical Challenges

In order to enable machine-to-machine communications and the realizationof the internet of things, the spectral efficiency for short bursts willhave to be improved, as well as the energy consumption of these devices(allowing for 10 years operation on the equivalent of 2 AA batteries).In current LTE systems, the network synchronization requirements place aburden on the devices to be almost continuously on. In addition, theefficiency goes down as the utilization per UE (user equipment, ormobile device) goes down. The PHY requirements for strictsynchronization between UE and eNB (Evolved Node B, or LTE base station)will have to be relaxed, enabling a re-designing of the MAC for IoTconnections that will simplify transitions from idle state to connectedstate.

Another important use case for cellular IoT (CIoT) is deep buildingpenetration to sensors and other devices, requiring an additional 20 dBor more of dynamic range. 5G CIoT solutions should be able to coexistwith the traditional high-throughput applications by dynamicallyadjusting parameters based on application context.

The path to higher spectral efficiency points towards a larger number ofantennas. A lot of research work has gone into full dimension andmassive MIMO architectures with promising results. However, the benefitsof larger MIMO systems may be hindered by the increased overhead fortraining, channel estimation and channel tracking for each antenna. APHY that is robust to channel variations will be needed as well asinnovative ways to reduce the channel estimation overhead.

Robustness to time variations is usually connected to the challengespresent in high Doppler use cases such as in vehicle-to-infrastructureand vehicle-to-vehicle automotive applications. With the expected use ofspectrum up to 60 GHz for 5G applications, this Doppler impact will bean order of magnitude greater than with current solutions. The abilityto handle mobility at these higher frequencies would be extremelyvaluable.

The OTFS Solution

OTFS is a modulation technique that modulates each information (e.g.,QAM) symbol onto one of a set of two dimensional (2D) orthogonal basisfunctions that span the bandwidth and time duration of the transmissionburst or packet. The modulation basis function set is specificallyderived to best represent the dynamics of the time varying multipathchannel.

OTFS transforms the time-varying multipath channel into a time invariantdelay-Doppler two dimensional convolution channel. In this way, iteliminates the difficulties in tracking time-varying fading, for examplein high speed vehicle communications.

OTFS increases the coherence time of the channel by orders of magnitude.It simplifies signaling over the channel using well studied AWGN codesover the average channel SNR. More importantly, it enables linearscaling of throughput with the number of antennas in moving vehicleapplications due to the inherently accurate and efficient estimation ofchannel state information (CSI). In addition, since the delay-dopplerchannel representation is very compact, OTFS enables massive MIMO andbeamforming with CSI at the transmitter for four, eight, and moreantennas in moving vehicle applications.

In deep building penetration use cases, one QAM symbol may be spreadover multiple time and/or frequency points. This is a key technique toincrease processing gain and in building penetration capabilities forCIoT deployment and PSTN replacement applications. Spreading in the OTFSdomain allows spreading over wider bandwidth and time durations whilemaintaining a stationary channel that does not need to be tracked overtime.

These benefits of OTFS will become apparent once the basic conceptsbehind OTFS are understood. There is a rich mathematical foundation ofOTFS that leads to several variations; for example it can be combinedwith OFDM or with multicarrier filter banks. In this paper we navigatethe challenges of balancing generality with ease of understanding asfollows:

This patent document describes the wireless Doppler multipath channeland its effects on multicarrier modulation.

This patent document also describes OTFS as a modulation that matchesthe characteristics of the time varying channel. We show OTFS can beimplemented as two processing steps:

A step that allows transmission over the time frequency plane, viaorthogonal waveforms generated by translations in time and/or frequency.In this way, the (time-varying) channel response is sampled over pointsof the time-frequency plane.

A pre-processing step using carefully crafted orthogonal functionsemployed over the time-frequency plane, which translate the time-varyingchannel in the time-frequency plane, to a time-invariant one in the newinformation domain defined by these orthogonal functions.

This patent document describes the new modulation scheme by exploringthe behavior of the channel in the new modulation domain in terms ofcoherence, time and frequency resolution etc.

This patent document describes aspects of channel estimation in the newinformation domain and multiplexing multiple users respectively,including complexity and implementation issues.

This patent document provides some performance results and we put theOTFS modulation in the context of cellular systems, discuss itsattributes and its benefits for 5G systems.

OTFS Modulation Over the Doppler Multipath Channel

The time variation of the channel introduces significant difficulties inwireless communications related to channel acquisition, tracking,equalization and transmission of channel state information (CSI) to thetransmit side for beamforming and MIMO processing. In this paper, wedevelop a modulation domain based on a set of orthonormal basisfunctions over which we can transmit the information symbols, and overwhich the information symbols experience a static, time invariant, twodimensional channel for the duration of the packet or bursttransmission. In that modulation domain, the channel coherence time isincreased by orders of magnitude and the issues associated with channelfading in the time or frequency domain in SISO or MIMO systems aresignificantly reduced.

The present document also discloses examples of a new modulation schemeby exploring the behavior of the channel in the new modulation domain interms of coherence, time and frequency resolution etc.

The document also discloses techniques for channel estimation in the newinformation domain and multiplexing multiple users respectively,including associated complexity and implementation issues forimplementing the disclosed techniques.

The present document also provides some performance results and examplesof benefits offered by the OTFS modulation in the context of cellularsystems, including 5G systems.

Example Models for a Wireless Channel

The multipath fading channel is commonly modeled in the baseband as aconvolution channel with a time varying impulse response:

r(t)=∫

(τ,t)s(t−τ)dτ  (1)

where s(t) and r(t) represent the complex baseband channel input andoutput respectively and where

(τ,t) is the complex baseband time varying channel response.

This representation, while general, may not explicitly give an insightinto the behavior and variations of the time varying impulse response. Amore useful and insightful model, which is also commonly used forDoppler multipath doubly fading channels is

r(t)=∫∫h(τ,v)e ^(j2πv(t−τ)) s(t−τ)dvdτ  (2)

In this representation, the received signal is a superposition ofreflected copies of the transmitted signal, where each copy is delayedby the path delay τ, frequency shifted by the Doppler shift v andweighted by the time-invariant delay-Doppler impulse response h(τ,v) forthat τ and v. In addition to the intuitive nature of thisrepresentation, Eq. (2) maintains the generality of Eq. (1) In otherwords it can represent complex Doppler trajectories, like acceleratingvehicles, reflectors etc. This can be seen by expressing the timevarying impulse response as a Fourier expansion with respect to the timevariable t

(τ,t)=∫h(τ,v)e ^(j2πvt) dt   (3)

Substituting (3) in (1) gives Eq. (2) after some manipulation. Morespecifically we obtain y(t)=∫∫

e{circumflex over ( )}j2πvτ h(τ,v) e{circumflex over ( )}j2πv(t−τ)x(t−τ)dvdτ

which differs from (2) by an exponential factor. The exponential factorcan be added to the definition of the impulse response h(τ,v) making thetwo representations equivalent. As an example, FIG. 1 shows thetime-varying impulse response for an accelerating reflector in the (τ,t)coordinate system, while FIG. 2 shows the same channel represented as atime invariant impulse response in the (τ,v) coordinate system.

An interesting feature revealed by these two figures is how compact the(τ,v) representation is compared to the (τ,t) representation. This hasimplications for channel estimation, equalization and tracking as willbe discussed later.

While h(τ,v) is, in fact, time-invariant, the operation on s(t) is stilltime varying, as can be seen by the effect of the explicit complexexponential function of time in Eq. (2). In one advantageous aspect,some embodiments of the modulation scheme based on appropriate choice oforthogonal basis functions, disclosed herein, render the effects of thischannel truly time-invariant in the domain defined by those basisfunctions.

The equation below represents a set of orthonormal basis functionsϕ_(τ,v)(t) indexed by τ,v which are orthogonal to translation andmodulation, i.e.,

ϕ_(τ,v)(t−τ ₀)=ϕ_(τ+τ) ₀ _(,v)(t)

e ^(j2πv) ⁰ ^(t)ϕ_(τ,v)(t)=ϕ_(τ,v−v) ₀ (t)   (4)

and a transmitted signal can be considered as a superposition of thesebasis functions:

s(t)=∫∫x(τ,v)ϕ_(τ,v)(t)dτdv   (5)

where the weights x(τ,v) represent the information bearing signal to betransmitted. After the transmitted signal of (5) goes through the timevarying channel of Eq. (2) a superposition of delayed and modulatedversions of the basis functions is obtained, which due to (4) resultsin:

=∫∫ϕ_(τ,v)(t){h(τ,v)*x(τ,v)}dτdv

where * denotes two dimensional convolution. Eq. (6) can be thought ofas a generalization of the derivation of the convolution relationshipfor linear time invariant systems, using one dimensional exponentials asbasis functions. The term in brackets can be recovered at the receiverby matched filtering against each basis function ϕ_(τ,v)(t). In thisway, a two dimensional channel relationship is established in the (τ,v)domain y(τ,v)=h(τ,v)*x(τ,v), where y(τ,v) is the receiver twodimensional matched filter output. In this domain, the channel isdescribed by a time invariant two-dimensional convolution.

A different interpretation of the wireless channel will also be usefulin what follows. Consider s(t) and r(t) as elements of the Hilbert spaceof square integrable functions

. Then Eq. (2) can be interpreted as a linear operator on

acting on the input s(t), parametrized by the impulse response h(τ,v),and producing the output r(t)

$\begin{matrix}{r = {{\prod_{h}(s)}:\mspace{31mu}{{s(t)} \in {\overset{{\prod\limits_{h}{( \cdot )}}\;}{\longrightarrow}{r(t)}} \in}}} & (7)\end{matrix}$

Although the operator is linear, it is not time-invariant. In theno-Doppler case, e.g., if h(v, τ)=h(0, τ)δ(v), then Eq. (2) reduces to atime invariant convolution. Also notice that while for time invariantsystems the impulse response is parameterized by one dimension, in thetime varying case we have a two dimensional impulse response. While inthe time invariant case the convolution operator produces asuperposition of delays of the input s(t), (hence the parameterizationis along the one dimensional delay axis) in the time varying case thereis a superposition of delay-and-modulate operations as seen in Eq. (2)(hence the parameterization is along the two dimensional delay andDoppler axes). This is a major difference which makes the time varyingrepresentation non-commutative (in contrast to the convolution operationwhich is commutative), and complicates the treatment of time varyingsystems.

The important point of Eq. (7) is that the operator Π_(h)(•) can becompactly parametrized in a two dimensional space h(τ,v), providing anefficient, time invariant description of the channel. Typical channeldelay spreads and Doppler spreads are a very small fraction of thesymbol duration and subcarrier spacing of multicarrier systems.

In the mathematics literature, the representation of time varyingsystems of (2) and (7) is sometimes called the Heisenbergrepresentation. It can be shown that every linear operator (7) can beparameterized by some impulse response as in (2).

OTFS Modulation Over the Doppler Multipath Channel

The time variation of the channel introduces significant difficulties inwireless communications related to channel acquisition, tracking,equalization and transmission of channel state information (CSI) to thetransmit side for beamforming and MIMO processing. This documentdiscloses a modulation domain based on a set of orthonormal basisfunctions over which systems can transmit the information symbols, andover which the information symbols experience a static, time invariant,two dimensional channel for the duration of the packet or bursttransmission. In this modulation domain, the channel coherence time isincreased by orders of magnitude and the issues associated with channelfading in the time or frequency domain in SISO or MIMO systems aresignificantly reduced.

Orthogonal Time Frequency Space (OTFS) modulation could be implementedas a cascade of two transformations. The first transformation maps thetwo dimensional plane where the information symbols reside (and which wecall the delay-Doppler plane) to the time frequency plane. The secondone transforms the time frequency domain to the waveform time domainwhere actual transmitted signal is constructed. This transform can bethought of as a generalization of multicarrier modulation schemes.

FIG. 3 provides a pictorial view of the two transformations that couldbe considered to constitute the OTFS modulation. It shows at a highlevel the signal processing steps that are required at the transmitterand receiver. It also includes the parameters that define each step,which will become apparent as we further expose each step. Further, FIG.4 shows a block diagram of the different processing stages at thetransmitter and receiver and establishes the notation that will be usedfor the various signals.

The Heisenberg Transform

One important aspect of transmission to construct an appropriatetransmit waveform which carries information provided by symbols on agrid in the time-frequency plane. In some embodiments, it isadvantageous to have a modulation scheme that transforms the channeloperation to an equivalent operation on the time-frequency domain withtwo properties:

-   -   The channel is orthogonalized on the time-frequency grid.    -   The channel time variation is simplified on the time-frequency        grid and can be addressed with an additional (or a single        additional) transform.

Fortunately, these goals can be accomplished with a scheme that is veryclose to well-known multicarrier modulation techniques, as explainednext. This is applicable to the general framework for multicarriermodulation and specifically examples of OFDM and multicarrier filterbank implementations.

Consider the following components of a time frequency modulation:

[1] A lattice or grid on the time frequency plane, that is a sampling ofthe time axis with sampling period T and the frequency axis withsampling period Δf.

Λ={(nT,mΔf),n,m∈

}  (8)

[2] A packet burst with total duration NT secs and total bandwidth MΔfHz

[3] A set of modulation symbols X[n,m], n=0, . . . , N−1, m=0, . . . ,M−1 we wish to transmit over this burst

[4] A transmit pulse g_(tr)(t) with the property of being orthogonal totranslations by T and modulations by Δf. This orthogonality property isuseful if the receiver uses the same pulse as the transmitter. In someimplementations, bi-orthogonality property may be used instead.

$\begin{matrix}{{< {g_{tr}(t)}},{{{{g_{tr}\left( {t - {nT}} \right)}e^{j2\pi m\Delta{f{({t - {nT}})}}}}>={\int{{g_{tr}^{*}(t)}{g_{r}\left( {t - {nT}} \right)}e^{j2\pi m\Delta{f{({t - {nT}})}}}{dt}}}} = {{\delta(m)}{\delta(n)}}}} & (9)\end{matrix}$

Given the above components, the time-frequency modulator is a Heisenbergoperator on the lattice A, that is, it maps the two dimensional symbolsX[n. m] to a transmitted waveform, via a superposition ofdelay-and-modulate operations on the pulse waveform g_(tr)(t)

$\begin{matrix}{{s(t)} = {\sum\limits_{m = {{- M}/2}}^{{M/2} - 1}{\sum\limits_{n = 0}^{N - 1}{{X\left\lbrack {n,m} \right\rbrack}{g_{tr}\left( {t - {nT}} \right)}e^{j\; 2\pi\; m\;\Delta\;{f{({t - {nT}})}}}}}}} & (10)\end{matrix}$

More formally

$\begin{matrix}{x = {{\prod_{x}\left( g_{tr} \right)}:\mspace{31mu}{{g_{tr}(t)} \in {\overset{\prod\limits_{X}\;{( \cdot )}}{\longrightarrow}{y(t)}} \in}}} & (11)\end{matrix}$

where Π_(x)(•) denotes the “discrete” Heisenberg operator, parameterizedby discrete values X[n,m].

Notice the similarity of (11) with the channel equation (7). This is notby coincidence, but rather because of application of a modulation effectthat mimics the channel effect, so that the end effect of the cascade ofmodulation and channel is more tractable at the receiver. For example,linear modulation (aimed at time invariant channels) is in its simplestform a convolution of the transmit pulse g(t) with a delta train of QAMinformation symbols sampled at the Baud rate T.

$\begin{matrix}{{s(t)} = {\sum\limits_{n = 0}^{N - 1}{{X\lbrack n\rbrack}{g\left( {t - {nT}} \right\rbrack}}}} & (12)\end{matrix}$

In the case of a time varying channel, systems convolve-and-modulate thetransmit pulse (c.f. the channel Eq. (2)) with a two dimensional deltatrain which samples the time frequency domain at a certain Baud rate andsubcarrier spacing.

The sampling rate in the time-frequency domain is related to thebandwidth and time duration of the pulse g_(tr)(t) namely itstime-frequency localization. In order for the orthogonality condition of(9) to hold for a frequency spacing Δf, the time spacing must be T≥1/Δf.The critical sampling case of T=1/Δf is generally not practical andrefers to limiting cases, for example to OFDM systems with cyclic prefixlength equal to zero or to filter banks with g_(tr)(t) equal to theideal Nyquist pulse.

Some examples are now in order:

Example 1: OFDM Modulation: Consider an OFDM system with M subcarriers,symbol length T_(OFDM), cyclic prefix length T_(CP) and subcarrierspacing 1/T_(OFDM). Substitute in Equation (10) symbol durationT=T_(OFDM)+T_(CP), number of symbols N=1, subcarrier spacingΔf=1/T_(OFDM) and g_(tr)(t) a square window that limits the duration ofthe subcarriers to the symbol length T

$\begin{matrix}{{g_{tr}(t)} = \left\{ \begin{matrix}{{1/\sqrt{T - T_{CP}}},} & {{- T_{CP}} < t < {T - T_{CP}}} \\{0,} & {else}\end{matrix} \right.} & (13)\end{matrix}$

Results in the OFDM formula below. Strictly speaking, the pulse of Eq.(10) is not orthonormal but is orthogonal to the receive filter (wherethe CP samples are discarded) as will be shown in this document.

$\begin{matrix}{{x(t)} = {\sum\limits_{m = {{- M}/2}}^{{M/2} - 1}{{X\left\lbrack {n,m} \right\rbrack}{g_{tr}(t)}e^{j\; 2\;\pi\; m\;{\Delta{ft}}}}}} & (14)\end{matrix}$

Example 2: Single Carrier Modulation: Equation (10) reduces to singlecarrier modulation with M=1 subcarrier, T equal to the Baud period andg_(tr)(t) equal to a square root raised cosine Nyquist pulse.

Example 3: Multicarrier Filter Banks (MCFB): Equation (10) describes aMCFB if g_(tr)(t) is a square root raised cosine Nyquist pulse withexcess bandwidth α, T is equal to the Baud period and Δf=(1+α)/T.

Expressing the modulation operation as a Heisenberg transform as in Eq.(11) may be counterintuitive. Modulation is usually considered to be atransformation of the modulation symbols X[m,n] to a transmit waveforms(t). The Heisenberg transform instead, uses X[m,n] asweights/parameters of an operator that produces s(t) when applied to theprototype transmit filter response g_(tr)(t)−c.f. Eq. (11). Whilepossibly counterintuitive, this formulation is useful in pursuing anabstraction of the modulation-channel-demodulation cascade effects in atwo dimensional domain where the channel can be described as timeinvariant.

On the receiver side, processing is performed to go back from thewaveform domain to the time-frequency domain. Since the received signalhas undergone the cascade of two Heisenberg transforms (one by themodulation effect and one by the channel effect), it is natural toinquire what the end-to-end effect of this cascade is. The answer tothis question is given by the following result:

Proposition 1: Let two Heisenberg transforms as defined by Eqs. (7), (2)be parametrized by impulse responses h₁(τ,v), h₂(τ,v) and be applied incascade to a waveform g(t)∈

. Then

Π_(h) ₂ (Π_(h) ₁ (g(t)))=Π_(h)(g(t))   (5)

where h(τ,v)=h₂(τ,v)⊙h₁(τ,v) is the “twisted” convolution of h₁(τ,v),h₂(τ,v) defined by the following convolve-and-modulate operation

h(τ,v)=∫∫h₂(τ′,v′)h ₁(τ−τ′,v−v′)e ^(j2πv′(τ−τ′)t) dτ′dv′  (16)

Proof: Provided elsewhere in the document.□

Applying the above result to the cascade of the modulation and channelHeisenberg transforms of (11) and (7), it can be shown that the receivedsignal is given by the Heisenberg transform

r(t)=Π_(f)(g _(tr)(t))=∫∫f(τ,v)e ^(j2πv(t−τ)) g _(tr)(t−τ)dvdτ+v(t)  (17)

where v(t) is additive noise and f(τ,v), the impulse response of thecombined transform, is given by the twisted convolution of X [n,m] andh(τ,v)

$\begin{matrix}{{f\left( {\tau,\nu} \right)} = {{{h\left( {\tau,\nu} \right)} \odot {X\left\lbrack {n,m} \right\rbrack}} = {\sum\limits_{m = {{- M}/w}}^{{M/2} - 1}{\sum\limits_{n = 0}^{N - 1}{{X\left\lbrack {n,m} \right\rbrack}{h\left( {{\tau - {nT}},{\nu - {m\;\Delta\; f}}} \right)}e^{j\; 2{\pi{({\nu - {m\;\Delta\; f}})}}{nT}}}}}}} & (18)\end{matrix}$

This result can be considered an extension of the single carriermodulation case, where the received signal through a time invariantchannel is given by the convolution of the QAM symbols with a compositepulse, that pulse being the convolution of the transmitter pulse and thechannel impulse response.

Receiver Processing and the Wigner Transform

Typical communication system design dictates that the receiver performsa matched filtering operation, taking the inner product of the receivedwaveform with the transmitter pulse, appropriately delayed or otherwisedistorted by the channel. Typical OTFS systems include a collection ofdelayed and modulated transmit pulses, and a receiver may perform amatched filter on each one of them. FIG. 5 provides a conceptual view ofthis processing. On the transmitter, a set of M subcarriers is modulatedfor each symbol transmitted, while on the receiver matched filtering isperformed on each of those subcarrier pulses. Define a receiver pulseg_(r)(t) and take the inner product with a collection of delayed andmodulated versions of it. The receiver pulse g_(r)(t) is in many casesidentical to the transmitter pulse, but we keep the separate notation tocover some cases where it is not (most notably in OFDM where the CPsamples have to be discarded).

While this approach will yield the sufficient statistics for datadetection in the case of an ideal channel, a concern can be raised herefor the case of non-ideal channel effects. In this case, the sufficientstatistics for symbol detection are obtained by matched filtering withthe channel-distorted, information-carrying pulses (assuming that theadditive noise is white and Gaussian). In many well designedmulticarrier systems however (e.g., OFDM and MCFB), the channeldistorted version of each subcarrier signal is only a scalar version ofthe transmitted signal, allowing for a matched filter design that isindependent of the channel and uses the original transmitted subcarrierpulse. The present document makes these statements more precise andexamines the conditions for this to be true.

FIG. 5 is only a conceptual illustration and does not point to theactual implementation of the receiver. Typically this matched filteringis implemented in the digital domain using an FFT or a polyphasetransform for OFDM and MCFB (multi-channel filter bank) respectively. Inthis paper we are rather more interested in the theoreticalunderstanding of this modulation. To this end, consider a generalizationof this matched filtering by taking the innerproduct<g_(r)(t−τ)e^(j2πv(t−τ)), r(t)>of the received waveform with thedelayed and modulated versions of the receiver pulse for arbitrary timeand frequency offset (τ,v). While this may not be a specificimplementation, it allows to view the operations of FIG. 5 as a twodimensional sampling of this more general inner product.

Define the inner product:

A _(g) _(r,) ^(r)(τ,v)=<g _(r)(t−τ)e ^(j2πv(t−τ)) ,r(t)>=∫g _(r)*(t−τ)e^(−j2πv(t−τ)) r(t)dt   (19)

The function A_(g) _(r,) ^(r)(τ,v) is known as the cross-ambiguityfunction in the radar and math communities and yields the matched filteroutput if sampled at τ=nT , v=mΔf (on the lattice Λ), i.e.,

Y[n,m]=A _(g) _(r,) ^(r)(τ,v)|_(τ=nT,v=mΔf)   (20)

In the math community, the ambiguity function is related to the inverseof the Heisenberg transform, namely the Wigner transform. FIG. 5provides an intuitive feel for that, as the receiver appears to invertthe operations of the transmitter. More formally, if a systems takes thecross-ambiguity or the transmit and receive pulses A_(g_r,g_tr) (τ,v),and uses it as the impulse response of the Heisenberg operator, then itcan obtain the orthogonal cross-projection operator

∏_(A_(gr,)g_(tr))(y(t)) = g_(tr)(t) < g_(r)(t), y(t)>

In words, the coefficients that come out of the matched filter, if usedin a Heisenberg representation, will provide the best approximation tothe original y(t) in the sense of minimum square error.

The key question here is what the relationship is between the matchedfilter output Y[n,m] (or more generally Y(τ,v)) and the transmitterinput X[n,m]. We have already established in (17) that the input to thematched filter r(t) can be expressed as a Heisenberg representation withimpulse response f(τ,v) (plus noise). The output of the matched filterthen has two contributions

Y(τ,v)=A _(g) _(r) _(,r)(τ,v)=A _(g) _(r) _(,[Π) _(f) _((g) _(tr)_()+v])(τ,v)=A _(g) _(r) _(,Π) _(f) _((g) _(tr) ₎(τ,v)+A _(g) _(r)_(,v)(τ,v)   (21)

The last term is the contribution of noise, which we can be denotes asV(τ,v)=A_(g) _(r) _(,v)(τ,v). The first term on the right hand side isthe matched filter output to the (noiseless) input comprising of asuperposition of delayed and modulated versions of the transmit pulse.One can next establish that this term can be expressed as the twistedconvolution of the two dimensional impulse response f(τ,v) with thecross-ambiguity function (or two dimensional cross correlation) of thetransmit and receive pulses.

The following theorem summarizes the result.

Theorem 1: (Fundamental time-frequency domain channel equation). If thereceived signal can be expressed as

Π_(f)(g _(tr)(t))∫∫f(σ,v)e ^(j2πv(t−τ)) g _(tr)(t−τ)dvdτ  (22)

Then the cross-ambiguity of that signal with the receive pulse g_(tr)(t)can be expressed as

A _(g) _(r) _(,Π) _(f) _((g) _(tr) )(τ,v)=f(τ, v)⊙A _(g) _(r) _(,g)_(tr) (τ,v)   (23)

Proof: is provided elsewhere in the present document.

Recall from (18) that f(τ,v)=h(τ,v)⊙x[n,m], that is, the compositeimpulse response is itself a twisted convolution of the channel responseand the modulation symbols.

Substituting f(τ,v) from (18) into (21) one can obtain the end-to-endchannel description in the time frequency domain:

$\begin{matrix}{{Y\left( {\tau,v} \right)} = {{{A_{g_{r,{\prod r}}{(g_{tr})}}\left( {\tau,v} \right)} + {V\left( {\tau,v} \right)}} = {{{h\left( {\tau,v} \right)} \odot {X\left\lbrack {n,\ m} \right\rbrack} \odot {A_{g_{r},g_{tr}}\left( {\tau,v} \right)}} + {V\left( {\tau,v} \right)}}}} & (24)\end{matrix}$

where V(τ,v) is the additive noise term. Eq. (24) provides anabstraction of the time varying channel on the time-frequency plane. Itstates that the matched filter output at any time and frequency point(τ,v) is given by the delay-Doppler impulse response of the channeltwist-convolved with the impulse response of the modulation operatortwist-convolved with the cross-ambiguity (or two dimensional crosscorrelation) function of the transmit and receive pulses.

Evaluating Eq. (24) on the lattice A one can obtain the matched filteroutput modulation symbol estimates

{circumflex over (X)}[m,n]=Y[n,m]=Y(τ,v)|_(τ=nT,v=mΔf)   (25)

In order to get more intuition on Equations (24), (25) first considerthe case of an ideal channel, i.e., h(τ,v)=δ(τ)δ(v). In this case bydirect substitution one can get the convolution relationship:

$\begin{matrix}{{Y\left\lbrack {n,\ m} \right\rbrack} = {{\sum\limits_{m^{\prime} = {{- M}/2}}^{{M/2} - 1}{\sum\limits_{n^{\prime} = 0}^{N - 1}{{X\left\lbrack {n^{\prime},m^{\prime}} \right\rbrack}{A_{g_{r},g_{tr}}\left( {{\left( {n - n^{\prime}} \right)T},\ {\left( {m - m^{\prime}} \right)\Delta\; f}} \right)}}}} + {V\left\lbrack {m,\ n} \right\rbrack}}} & (26)\end{matrix}$

In order to simplify Eq. (26) one can use the orthogonality propertiesof the ambiguity function. Since implementations may in general usedifferent transmit and receive pulses one can modify the orthogonalitycondition on the design of the transmit pulse we stated in (9) to abi-orthogonality condition:

$\begin{matrix}{{< {g_{tr}(t)}},{{{g_{r}\left( {t - {nT}} \right)}e^{j\; 2\pi\; m\;\Delta\;{f{({t - {nT}})}}}} > {\quad{= {{\int{{g_{tr}^{*}(t)}{g_{r}\left( {t - {nT}} \right)}e^{j\; 2\pi\; m\;\Delta\;{f{({t - {nT}})}}}{dt}}} = {{\delta(m)}{\delta(n)}}}}}}} & (27)\end{matrix}$

Under this condition, only one term survives in (26) and resulting in:

Y[n,m]=X[n,m]+V[n,m]  (28)

where V[n,m] is the additive white noise. Eq. (28) shows that thematched filter output does recover the transmitted symbols (plus noise)under ideal channel conditions. Of more interest of course is the caseof non-ideal time varying channel effects. This document shows that,even in this case, the channel orthogonalization is maintained (nointersymbol or intercarrier interference), while the channel complexgain distortion has a closed form expression.

The following theorem summarizes the result as a generalization of (28).

Theorem 2: (End-to-end time-frequency domain channel equation):

If h(τ,v) has finite support bounded by (τ_(max), v_(max)) and if A_(g)_(r) _(,g) _(tr) (τ,v)=0 for τ∈(nT−τ_(max), nT+τ_(max), v∈(mΔf−v_(max),,mΔf+v_(max)), that is, the ambiguity function bi-orthogonality propertyof (27) is true in a neighborhood of each grid point (mΔf ,nT) of thelattice A at least as large as the support of the channel responseh(τ,v), then the following equation holds

Y[n,m]=H[n,m]X[n,m]  (29)

H[n,m]=∫∫h(τ,v)e ^(j2πvnT) e ^(−j2π(v+mΔf)τ) dvdτ

If the ambiguity function is only approximately bi-orthogonal in theneighborhood of Λ (by continuity), then (29) is only approximately true

Proof: provided elsewhere in the present document.

Eq. (29) is a fundamental equation that describes the channel behaviorin the time-frequency domain. It is the basis for understanding thenature of the channel and its variations along the time and frequencydimensions.

Some observations are now in order on Eq. (29). As mentioned before,there is no interference across X[n,m] in either time n or frequency m.

The end-to-end channel distortion in the modulation domain is a(complex) scalar that should be equalized.

If there is no Doppler, i.e. h(τ,v)=h(τ,0)δ(v), then Eq. (29) becomes

$\begin{matrix}{{Y\left\lbrack {n,\ m} \right\rbrack} = {{{X\left\lbrack {n,\ m} \right\rbrack}{\int{{h\left( {\tau,0} \right)}e^{{- j}\; 2\pi\; m\;\Delta\; f\;\tau_{dt}}}}} = {{X\left\lbrack {n,\ m} \right\rbrack}{H\left( {0,\ {m\;\Delta\; f}} \right)}}}} & (30)\end{matrix}$

which is the well-known multicarrier result, that each subcarrier symbolis multiplied by the frequency response of the time invariant channelevaluated at the frequency of that subcarrier.

If there is no multipath, i.e. h(τ,v)=h(0,v)δ(τ), then Eq. (29) becomes

Y[n,m]=X[n,m]∫h(v, 0)e ^(j2πvnT) dτ

Notice that the fading each subcarrier experiences as a function of timenT has a complicated expression as a weighted superposition ofexponentials. This is a major complication in the design of wirelesssystems with mobility like LTE; it necessitates the transmission ofpilots and the continuous tracking of the channel, which becomes moredifficult the higher the vehicle speed or Doppler bandwidth is.

Some examples of this general framework include the following.

Example 3: (OFDM modulation). In this case the fundamental transmitpulse is given by (13) and the fundamental receive pulse is

$\begin{matrix}{{g_{r}(t)} = \left\{ \begin{matrix}0 & {{- T_{CP}} < t < o} \\\frac{1}{\sqrt{T - T_{CP}}} & {0 < t < {T - T_{CP}}} \\0 & {else}\end{matrix} \right.} & (32)\end{matrix}$

i.e., the receiver zeroes out the CP samples and applies a square windowto the symbols comprising the OFDM symbol. It is worth noting that inthis case, the bi-orthogonality property holds exactly along the timedimension. FIG. 6 shows the cross correlation between the transmit andreceive pulses of (13) and (32). The cross correlation is exactly equalto one and zero in the vicinity of zero and ±T respectively, whileholding those values for the duration of T_(CP). Hence, as long as thesupport of the channel on the time dimension is less than T_(CP) thebi-orthogonality condition is satisfied along the time dimension. Acrossthe frequency dimension the condition is only approximate, as theambiguity takes the form of a sinc function as a function of frequencyand the nulls are not identically zero for the whole support of theDoppler spread.

Example 4: (MCFB modulation). In the case of multicarrier filter banksg_(tr)(t)=g_(r)(t)=g(t). There are several designs for the fundamentalpulse g(t). A square root raised cosine pulse provides good localizationalong the frequency dimension at the expense of less localization alongthe time dimension. If T is much larger than the support of the channelin the time dimension, then each subchannel sees a flat channel and thebi-orthogonality property holds approximately.

In summary, it will be appreciated that the one of the two transformsthat define OTFS have been described. How the transmitter and receiverapply appropriate operators on the fundamental transmit and receivepulses and orthogonalize the channel according to Eq. (29) has also beendisclosed. One can see by examples how the choice of the fundamentalpulse affect the time and frequency localization of the transmittedmodulation symbols and the quality of the channel orthogonalization thatis achieved. However, Eq. (29) shows that the channel in this domain,while free of intersymbol interference, suffers from fading across boththe time and the frequency dimensions via a complicated superposition oflinear phase factors.

The next section starts from Eq. (29) and describes the second transformthat defines OTFS. This section shows how that transform defines aninformation domain where the channel does not fade in either dimension.

The 2D OTFS Transform

The time-frequency response H[n,m] in (29) is related to the channeldelay-Doppler response h(r, v) by an expression that resembles a Fouriertransform. However, there are two important differences: (i) thetransform is two dimensional (along delay and Doppler) and (ii) theexponentials defining the transforms for the two dimensions haveopposing signs. Despite these difficulties, Eq. (29) points in thedirection of using complex exponentials as basis functions on which tomodulate the information symbols; and only transmit on thetime-frequency domain the superposition of those modulated complexexponential bases. This is the approach generally used herein.

This is similar to the SC-FDMA modulation scheme, where in the frequencydomain we transmit a superposition of modulated exponentials (the outputof the DFT preprocessing block). The reason to pursue this direction isto exploit Fourier transform properties and translate a multiplicativechannel in one Fourier domain to a convolution channel in the otherFourier domain.

Given the difficulties of Eq. (29) mentioned above, a suitable versionof Fourier transform and associated sampling theory results is useful.The following definitions can be used:

Definition 1: Symplectic Discrete Fourier Transform: Given a squaresummable two dimensional sequence X[m,n]∈

(Λ) we define

$\begin{matrix}{{x\left( {\tau,v} \right)} = {\sum\limits_{m,n}^{\;}{{X\left\lbrack {n,m} \right\rbrack}e^{{- j}\; 2{\pi{({{vnT} - {\tau\; m\;\Delta\; f}})}}}\mspace{11mu} S\; D\; F\;{T\left( {X\left\lbrack {n,m} \right\rbrack} \right)}}}} & (33)\end{matrix}$

The above 2D Fourier transform (sometimes known as the SymplecticDiscrete Fourier Transform in the math community) differs from the morewell known Cartesian Fourier transform in that the exponential functionsacross each of the two dimensions have opposing signs. This is necessaryin this case, as it matches the behavior of the channel equation.

Further notice that the resulting x(τ,v) is periodic with periods (1/Δf,1/T) . This transform defines a new two dimensional plane, which we willcall the delay-Doppler plane, and which can represent a max delay of1/Δf and a max Doppler of 1/T. A one dimensional periodic function isalso called a function on a circle, while a 2D periodic function iscalled a function on a torus (or donut). In this case x(τ,v) is definedon a torus Z with circumferences (dimensions) (1/Δf, 1/T).

The periodicity of x(τ,v) (or sampling rate of the time-frequency plane)also defines a lattice on the delay-Doppler plane, which can be calledthe reciprocal lattice

$\begin{matrix}{⩓^{\bot}{= \left\{ {\left( {{m\frac{1}{\Delta\; f}},{n\frac{1}{T}}} \right),n\;,{m \in {\mathbb{Z}}}} \right\}}} & (34)\end{matrix}$

The points on the reciprocal lattice have the property of making theexponent in (33), an integer multiple of 2π.

The inverse transform is given by:

$\begin{matrix}{{X\left\lbrack {n,m} \right\rbrack} = {\frac{1}{c}{\overset{\frac{1}{\Delta\; f}}{\int\limits_{0}}{\overset{\frac{1}{T}}{\int\limits_{0}}{{x\left( {\tau,v} \right)}e^{{j2}\;{\pi{({{vnT} - {\tau\; m\;\Delta\; f}})}}}{dv}\mspace{11mu} d\;\tau\; S\; D\; F\;{T^{- 1}\left( {x\left( {\tau,v} \right)} \right)}}}}}} & (35)\end{matrix}$

where c=TΔf.

Next, define a sampled version of x(τ,v). In particular, a version thatcan take M samples on the delay dimension (spaced at 1/MΔf) and Nsamples on the Doppler dimension (spaced at 1/NT). More formally wedefine a denser version of the reciprocal lattice

$\begin{matrix}{⩓_{0}^{\bot}{= \left\{ {\left( {{m\frac{1}{M\;\Delta\; f}},{n\frac{1}{NT}}} \right),n\;,{m \in {\mathbb{Z}}}} \right\}}} & (36)\end{matrix}$

So that Λ^(⊥)⊆Λ₀ ^(⊥). Define discrete periodic functions on this denselattice with period (1/Δf, 1/T), or equivalently define functions on adiscrete torus with these dimensions

$\begin{matrix}{Z_{0}^{\bot} = \left\{ {\left( {{m\frac{1}{M\Delta f}},\ {n\frac{1}{NT}}} \right),\ {m = 0},\ldots\mspace{14mu},{M - 1},\ {n = 0},{{\ldots\mspace{14mu} N} - 1},} \right\}} & (37)\end{matrix}$

These functions are related via Fourier transform relationships todiscrete periodic functions on the lattice Λ, or equivalently, functionson the discrete torus

Z ₀={(nT, mΔf),m=0, . . . , M−1,n=0, . . . N−1,}  (38)

It is useful to develop an expression for sampling Eq. (33) on thelattice of (37). First, start with the following definition.

Definition 2: Symplectic Finite Fourier Transform: If X_(p)[k,l] isperiodic with period (N,M), then we define

$\begin{matrix}{{x_{p}\left\lbrack {m,n} \right\rbrack} = {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{l = {- \frac{M}{2}}}^{\frac{M}{2} - 1}{{X_{p}\left\lbrack {k,l} \right\rbrack}e^{{- j}\; 2\;{\pi{({\frac{nk}{N} - \frac{ml}{M}})}}}\; S\; F\; F\; T\;\left( {X\left\lbrack {k,l} \right\rbrack} \right)}}}} & (39)\end{matrix}$

Notice that x_(p)[m,n] is also periodic with period [M,N] orequivalently, it is defined on the discrete torus Z₀ ^(⊥). Formally, theSFFT (X[n,m]) is a linear transformation from

(Z₀)→

(Z₀ ^(⊥)).

Now consider generating x_(p)[m,n] as a sampled version of (33), i.e.,

${x_{p}\left\lbrack {m,n} \right\rbrack} = {{x\left\lbrack {m,n} \right\rbrack} = {{x\left( {\tau,v} \right)}❘_{{\tau = \frac{m}{M\;\Delta\; f}},{v = \frac{n}{NT}}}.}}$

Then it can be shown that (39) still holds where X_(p)[m,n] is aperiodization of X [n,m] with period (N,M)

$\begin{matrix}{{X_{p}\left\lbrack {n,\ m} \right\rbrack} = {\sum\limits_{l,{k = {- \infty}}}^{\infty}{X\left\lbrack {{n - {kN}},\ {m - {lM}}} \right\rbrack}}} & (40)\end{matrix}$

This is similar to the well-known result that sampling in one Fourierdomain creates aliasing in the other domain.

The inverse discrete (symplectic) Fourier transform is given by

$\begin{matrix}{{X_{p}\left\lbrack {n,m} \right\rbrack} = {\frac{1}{MN}{\sum\limits_{l,k}^{\;}{{x\left\lbrack {l,k} \right\rbrack}e^{j\; 2{\pi{({\frac{nk}{N} - \frac{ml}{M}})}}}S\; F\; F\;{T^{- 1}\left( {x\left\lbrack {l,k} \right\rbrack} \right)}}}}} & (41)\end{matrix}$

where l=0, . . . ,M−1, k=0, . . . ,N−1. If the support of X[n,m] istime-frequency limited to Z₀ (no aliasing in (40)), then X_(p)[n,m]=X[n,m] for n,m∈Z₀, and the inverse transform (41) recovers the originalsignal.

In the math community, the SDFT is called “discrete” because itrepresents a signal using a discrete set of exponentials, while the SFFTis called “finite” because it represents a signal using a finite set ofexponentials.

Arguably the most important property of the symplectic Fourier transformis that it transforms a multiplicative channel effect in one domain to acircular convolution effect in the transformed domain. This issummarized in the following proposition:

Proposition 2: Let X₁[n,m]∈

(Z₀), X₂[n,m]∈

(Z₀) be periodic 2D sequences. Then

SFFT(X ₁ [n,m]*X ₂ [n,m])=SFFT(X ₁ [n,m])·SFFT(X₂ [n,m])   (42)

where * denotes two dimensional circular convolution.

Proof: is provided elsewhere in this document.

Discrete OTFS modulation: Consider a set of NM QAM information symbolsarranged on a 2D grid x[l,k]=0, . . . ,N−1, l=0, . . . ,M−1 that atransmitter wants to transmit. Without loss of generality, considerx[l,k] to be two dimensional periodic with period [N,M]. Further, assumea multicarrier modulation system defined by

[A] A lattice on the time frequency plane, that is a sampling of thetime axis with sampling period T and the frequency axis with samplingperiod Δf (c.f. Eq. (8)).

[B] A packet burst with total duration NT secs and total bandwidth MΔfHz.

[C] Transmit and receive pulses g_(tr)(t), g_(tr)(t)∈L₂(

) satisfying the bi-orthogonality property of (27)

[D] A transmit windowing square summable function W_(tr)[n,m]∈

(Λ) multiplying the modulation symbols in the time-frequency domain

[E] A set of modulation symbols X[n,m], n=0, . . . ,N−1, m=0, . . . ,M−1related to the information symbols x[k,l] by a set of basis functionsb_(k,l)[n,m]

$\begin{matrix}{{X\left\lbrack {n,m} \right\rbrack} = {{\frac{1}{MN}{W_{tr}\left\lbrack {n,m} \right\rbrack}{\sum\limits_{k = 0}^{N - 1}{\sum\limits_{l = 0}^{M - 1}{{x\left\lbrack {l,k} \right\rbrack}{b_{k,l}\left\lbrack {n,m} \right\rbrack}{b_{k,l}\left\lbrack {n,m} \right\rbrack}}}}} = e^{j\; 2{\pi{({\frac{ml}{M} - \frac{nk}{N}})}}}}} & (43)\end{matrix}$

where the basis functions b_(k,l)[n,m] are related to the inversesymplectic Fourier transform (c.f., Eq. (41))

Given the above components, define the discrete OTFS modulation via thefollowing two steps

X[n,m]=W _(tr) [n,m]SFFT ⁻¹(x[k,l])

s(t)=Π_(x)(g _(tr)(t)   (44)

The first equation in (44) describes the OTFS transform, which combinesan inverse symplectic transform with a widowing operation. The secondequation describes the transmission of the modulation symbols X[n,m] viaa Heisenberg transform of g_(tr)(t) parameterized by X[n,m]. Moreexplicit formulas for the modulation steps are given by Equations (41)and (10).

While the expression of the OTFS modulation via the symplectic Fouriertransform reveals important properties, it is easier to understand themodulation via Eq. (43), that is, transmitting each information symbolx[k,l] by modulating a 2D basis function b_(k,l)[n,m] on thetime-frequency plane.

FIG. 7 visualizes this interpretation by isolating each symbol in theinformation domain and showing its contribution to the time-frequencymodulation domain. Of course the transmitted signal is the superpositionof all the symbols on the right (in the information domain) or all thebasis functions on the left (in the modulation domain).

FIG. 7 uses the trivial window W_(tr)[n,m]=1 for all n=0, . . . ,N−1,

${m = {- \frac{M}{2}}},{{.\;.\;.\;\frac{M}{2}} - 1}$

and zero else. This may seem superfluous but there is a technical reasonfor this window: recall that SFFT⁻¹(x[k,l]) is a periodic sequence thatextends to infinite time and bandwidth. By applying the windowtransmitters can limit the modulation symbols to the available finitetime and bandwidth. The window in general could extend beyond the periodof the information symbols [M,N] and could have a shape different from arectangular pulse. This would be akin to adding cyclic prefix/suffix inthe dimensions of both time and frequency with or without shaping. Thechoice of window has implications on the shape and resolution of thechannel response in the information domain as we will discuss later. Italso has implications on the receiver processing as the potential cyclicprefix/suffix has to either be removed or otherwise handled.

Discrete OTFS demodulation: Assume that the transmitted signal s(t)undergoes channel distortion according to (7), (2) yielding r(t) at thereceiver. Further, let the receiver employ a receive windowing squaresummable function W_(r)[n,m]. Then, the demodulation operation consistsof the following steps:

(i) Matched filtering with the receive pulse, or more formally,evaluating the ambiguity function on Λ (Wigner transform) to obtainestimates of the time-frequency modulation symbols

Y[n,m]=A _(g) _(r) _(,y)(τ,v)|_(τ=nT,v=mΔf)   (45)

(ii) windowing and periodization of Y[n,m]

$\begin{matrix}{{{Y_{w}\left\lbrack {n,m} \right\rbrack} = {{W_{r}\left\lbrack {n,m} \right\rbrack}{Y\left\lbrack {n,m} \right\rbrack}}}{{Y_{p}\left\lbrack {n,m} \right\rbrack} = {\sum\limits_{k,{l = {- \infty}}}^{\infty}{Y_{w}\left\lbrack {{n - {kN}},{m - {lM}}} \right\rbrack}}}} & (46)\end{matrix}$

(iii) and applying the symplectic Fourier transform on the periodicsequence Y_(p)[n,m]

{circumflex over (x)}[l,k]=y[l,k]=SFFT(Y _(p) [n,m])   (47)

The first step of the demodulation operation can be interpreted as amatched filtering operation on the time-frequency domain as discussedearlier. The second step is there to ensure that the input to the SFFTis a periodic sequence. If a trivial window is used, this step can beskipped. The third step can also be interpreted as a projection of thetime-frequency modulation symbols on the orthogonal basis functions

$\begin{matrix}{{{\overset{\hat{}}{x}\left\lbrack {l,k} \right\rbrack} = {\sum\limits_{m = 0}^{M - 1}{\sum\limits_{n = 0}^{N - 1}{{\overset{\hat{}}{X}\left( {n,m} \right)}{b_{k,l}^{*}\left( {n,m} \right)}}}}}{{b_{k,l}^{*}\left( {n,m} \right)} = e^{{- j}2{\pi{({\frac{lm}{L} - \frac{kn}{K}})}}}}} & (48)\end{matrix}$

The discrete OTFS modulation defined above points to efficientimplementation via discrete-and-periodic FFT type processing. However,it may not provide insight into the time and bandwidth resolution ofthese operations in the context of two dimensional Fourier samplingtheory. This document introduces the continuous OTFS modulation andrelate the more practical discrete OTFS as a sampled version of thecontinuous modulation.

Continuous OTFS modulation: Consider a two dimensional periodic functionx(τ,v) with period [1/Δf, 1/T] that a transmitter wants to transmit; thechoice of the period may seem arbitrary at this point, but it willbecome clear after the discussion next. Further, assume a multicarriermodulation system defined by

(A) A lattice on the time frequency plane, that is a sampling of thetime axis with sampling period T and the frequency axis with samplingperiod Δf (c.f. Eq. (8)).

(B) Transmit and receive pulses g_(tr)(t), g_(tr)(t)∈L₂(

) satisfying the bi-orthogonality property of (27)

(C) A transmit windowing function W_(tr)[n,m]∈

(Λ) multiplying the modulation symbols in the time-frequency domain

Given the above components, define the continuous OTFS modulation viathe following two steps

X[n,m]=W _(tr) [n,m]SDFT⁻¹(x(τ,v))

s(t)=Π_(x)(g _(tr)(t))   (49)

The first equation describes the inverse discrete time-frequencysymplectic Fourier transform [c.f. Eq. (35)] and the windowing function,while the second equation describes the transmission of the modulationsymbols via a Heisenberg transform [c.f. Eq. 10)].

Continuous OTFS demodulation: Assume that the transmitted signal s(t)undergoes channel distortion according to (7), (2) yielding r(t) at thereceiver. Further, let the receiver employ a receive windowing functionW_(r)[n,m]␣

(Λ). Then, the demodulation operation consists of two steps:

(i) Evaluating the ambiguity function on A (Wigner transform) to obtainestimates of the time-frequency modulation symbols

y[n,m]=A _(g) _(r) _(,y)(τ,v)|_(τ=nT,v=mΔf)   (50)

(ii) Windowing and applying the symplectic Fourier transform on themodulation symbols

{circumflex over (x)}(τ,v)=SDFT(W _(r) [n,m]Y[n,m])   (51)

In (50), (51) there is no periodization of Y[n,m], since the SDFT isdefined on aperiodic square summable sequences. The periodization stepneeded in discrete OTFS can be understood as follows. Suppose we wish torecover the transmitted information symbols by performing a continuousOTFS demodulation and then sampling on the delay-Doppler grid

${\overset{\hat{}}{x}\left( {l,k} \right)} = \left. {\overset{\hat{}}{x}\left( {\tau,v} \right)} \right|_{{\tau = \frac{m}{M\Delta f}},{v = \frac{n}{NT}}}$

Since performing a continuous symplectic Fourier transform is notpractical, consider whether the same result can be obtained using SFFT.The answer is that SFFT processing will produce exactly the samples asif the input sequence is first periodized (aliased)—see also (39) (40).

The description so covers all the steps of the OTFS modulation asdepicted in FIG. 3. The document has also discussed how the Wignertransform at the receiver inverts the Heisenberg transform at thetransmitter [c.f. Eqs. (26), (28)], and similarly for the forward andinverse symplectic Fourier transforms. The practical question is whatform the end-to-end signal relationship takes when a non-ideal channelis between the transmitter and receiver.

Channel Equation in the OTFS Domain

The main result in this section shows how the time varying channel in(2), (7), is transformed to a time invariant convolution channel in thedelay Doppler domain.

Proposition 3: Consider a set of NM QAM information symbols arranged ina 2D periodic sequence x[l,k] with period [M,N]. The sequence x[k,l]undergoes the following transformations:

(a) It is modulated using the discrete OTFS modulation of Eq. (44).

(b) It is distorted by the delay-Doppler channel of Eqs.(2), (7).

(c) It is demodulated by the discrete OTFS demodulation of Eqs. (45),(47).

The estimated sequence {circumflex over (x)}[l,k] obtained afterdemodulation is given by the two dimensional periodic convolution

$\begin{matrix}{{\overset{\hat{}}{x}\left\lbrack {l,k} \right\rbrack} \simeq {\frac{1}{MN}{\sum\limits_{m = 0}^{M - 1}{\sum\limits_{n = 0}^{N - 1}{{x\left\lbrack {m,n} \right\rbrack}{h_{w}\left( {\frac{l - m}{M\Delta f},\frac{k - n}{NT}} \right)}}}}}} & (52)\end{matrix}$

of the input QAM sequence x[m,n] and a sampled version of the windowedimpulse response h_(w)(•),

$\begin{matrix}{{h_{w}\left( {\frac{l - m}{M\Delta f},\frac{k - n}{NT}} \right)} = {{h_{w}\left( {\tau^{\prime},v^{\prime}} \right)}❘_{{\tau^{\prime} = \frac{l - m}{M\Delta f}},{v^{\prime} = \frac{k - n}{NT}}}}} & (53)\end{matrix}$

where h_(w)(τ′, v′) denotes the circular convolution of the channelresponse with a windowing function. To be precise, in the window w(τ,v)is circularly convolved with a slightly modified version of the channelimpulse response e{circumflex over ( )}(−j2πvτ) h(τ,v) (by a complexexponential) as can be seen in the equation.

h _(w)(τ′,v′)=∫∫e ^(−j2πvτ) h(τ,v)w(τ′−τ,v′−v)dτdv   (54)

where the windowing function w(τ,v) is the symplectic Fourier transformof the time-frequency window W[n,m]

$\begin{matrix}{{W\left( {\tau,v} \right)} = {\sum\limits_{m = 0}^{M - 1}{\sum\limits_{n = 0}^{N - 1}{{W\left\lbrack {n,m} \right\rbrack}e^{- {{j2\pi}{({{vnT} - {\tau m\Delta f}})}}}}}}} & (55)\end{matrix}$

and where W[n,m] is the product of the transmit and receive window.

W[n,m]=W_(tr)[n,m]W_(r)[n,m]  (56)

Proof: provided elsewhere in the document.

In many cases, the windows in the transmitter and receiver are matched,i.e., W_(tr)[n,m]=W₀[n,m] and W_(r)[n,m]=W₀*[n,m], henceW[n,m]=|[n,m]|².

The window effect is to produce a blurred version of the originalchannel with a resolution that depends on the span of the frequency andtime samples available as will be discussed in the next section. If weconsider the rectangular (or trivial) window, i.e., W[n,m]=1, n=0, . . ., N−1, m=−M/2, . . . , M/2−1 and zero else, then its SDFT w(τ,v) in (55)is the two dimensional Dirichlet kernel with bandwidth inverselyproportional to N and M.

There are several other uses of the window function. The system can bedesigned with a window function aimed at randomizing the phases of thetransmitted symbols, akin to how QAM symbol phases are randomized inWiFi and Multimedia-Over-Coax communication systems. This randomizationmay be more important for pilot symbols than data carrying symbols. Forexample, if neighboring cells use different window functions, theproblem of pilot contamination is avoided.

A different use of the window is the ability to implement random accesssystems over OTFS using spread spectrum/CDMA type techniques as will bediscussed later.

Channel Time/Frequency Coherence and OTFS Resolution

This section discloses, among other things, certain OTFS design issues,including the choice of data frame length, bandwidth, symbol length andnumber of subcarriers. We study the tradeoffs among these parameters andgain more insight on the capabilities of OTFS technology.

Since OTFS is based on Fourier representation theory similar spectralanalysis concepts apply like frequency resolution vs Fourier transformlength, sidelobes vs windowing shape etc. One difference that can be asource of confusion comes from the naming of the two Fourier transformdomains in the current framework.

OTFS transforms the time-frequency domain to the delay-Doppler domaincreating the Fourier pairs: (i) time⇔Doppler and (ii) frequency⇔delay.The “spectral” resolution of interest here therefore is either on theDoppler or on the delay dimensions.

These issues can be easier clarified with an example. Consider atime-invariant multipath channel (zero Doppler) with frequency responseH(f,0) for all t. In the first plot of FIG. 8 we show the real part ofH(f,0) as well as a sampled version of it on a grid of M=8 subcarriers.The second plot of FIG. 8 shows the SDFT of the sampled H(mΔf,0), i.e.,h(τ,0) along the delay dimension. Taking this frequency response to the“delay” domain reveals the structure of this multipath channel, that is,the existence of two reflectors with equal power in this example.Further, the delay dimension of the SDFT is periodic with period 1/Δf asexpected due to the nature of the discrete Fourier transform. Finally,in the third plot of FIG. 8 we shows the SFFT of the frequency response,which as expected is a sampled version of the SDFT of the second plot.The SFFT has M=8 points in each period 1/Δf leading to a resolution inthe delay domain of 1/MΔf=1/BW.

In the current example, the reflectors are separated by more than 1/MΔfand are resolvable. If they were not, then the system would experience aflat channel within the bandwidth of observation, and in the delaydomain the two reflectors would have been blurred into one.

FIG. 9 shows similar results for a flat Doppler channel with timevarying frequency response H(0,t) for all f. The first plot shows theresponse as a function of time, while the second plot shown the SDFTalong the Doppler dimension. Finally, the third plot shows the SFFT,that is the sampled version of the transform. Notice that the SDFT isperiodic with period 1/T while the SFFT is periodic with period 1/T andhas resolution of 1/NT.

The conclusion one can draw from FIG. 9 is that as long as there issufficient variability of the channel within the observation time NT,that is, as long as reflectors have Doppler frequency difference largerthan 1/NT, the OTFS system will resolve these reflectors and willproduce an equivalent channel in the delay-Doppler domain that is notfading. In other words, OTFS can take a channel that inherently has acoherence time of only T and produce an equivalent channel in the delayDoppler domain that has coherence time NT. This is an important propertyof OTFS as it can increase the coherence time of the channel by ordersof magnitude and enable MIMO processing and beamforming under Dopplerchannel conditions.

The two one-dimensional channel examples previously discussed arespecial cases of the more general two-dimensional channel of FIG. 10.The time-frequency response and its sampled version are shown in FIG.10, where the sampling period is (T, Δf).

FIG. 11 shows the SDFT of this sampled response which is periodic withperiod (1/T, 1/Δf), across the Doppler and delay dimensionsrespectively.

The Nyquist sampling requirements for this channel response may bequantified as follows. 1/T is generally on the order of Δf (for an OFDMsystem with zero length CP it is exactly 1/T=Δf) so the period of thechannel response in FIG. 11 is approximately (Δf, T), and aliasing canbe avoided as long as the support of the channel response is less than±Δf/2 in the Doppler dimension and ±T/2 in the delay dimension.

FIG. 12 shows the SFFT, that is, the sampled version of FIG. 11. Theresolution of FIG. 11 is 1/NT, 1/MΔf across the Doppler and delaydimensions respectively.

FIG. 13 summarizes the sampling aspects of the OTFS modulation. The OTFSmodulation consists of two steps shown in this figure:

A Heisenberg transform translates a time-varying convolution channel inthe waveform domain to an orthogonal but still time varying channel inthe time frequency domain. For a total bandwidth BW and M subcarriersthe frequency resolution is Δf=BW/M. For a total frame duration T_(f)and N symbols the time resolution is T=T_(f)/N.

A SFFT transform translates the time-varying channel in thetime-frequency domain to a time invariant one in the delay-Dopplerdomain. The Doppler resolution is 1/T_(f) and the delay resolution is1/BW.

The choice of window can provide a tradeoff between main lobe width(resolution) and side lobe suppression, as in classical spectralanalysis.

Channel Estimation in the OTFS Domain

There is a variety of different ways a channel estimation scheme couldbe designed for an OTFS system, and a variety of differentimplementation options and details. This section presents, among otherthings, a high level summary and highlight some of the concepts.

A straightforward way to perform channel estimation entails transmittinga sounding OTFS frame containing a discrete delta function in the OTFSdomain or equivalently a set of unmodulated carriers in the timefrequency domain. From a practical standpoint, the carriers may bemodulated with known, say BPSK, symbols which are removed at thereceiver as is common in many OFDM systems. This approach could beconsidered an extension of the channel estimation symbols used in WiFiand Multimedia-Over-Coax modems.

FIG. 14 shows an example of an OTFS symbol containing such an impulse.This approach may however be wasteful as the extend of the channelresponse is only a fraction of the full extend of the OTFS frame (1/T,1/Δf). For example, in LTE systems 1/T≈15 KHz while the maximum Dopplershift f_(d,max) is typically one to two orders of magnitude smaller.Similarly 1/Δf≈67 usec, while maximum delay spread τ_(max) is again oneto two orders of magnitude less. Implementations thus can have a muchsmaller region of the OTFS frame devoted to channel estimation while therest of the frame carries useful data. More specifically, for a channelwith support (±f_(d,max), ±τ_(max)) an OTFS subframe of length(2f_(d,max)/T, 2τ_(max)/Δf) may have to be used.

In the case of multiuser transmission, each UE can have its own channelestimation subframe positioned in different parts of the OTFS frame.This is akin to multiplexing of multiple users when transmitting UplinkSounding Reference Signals in LTE. The difference is that OTFS benefitsfrom the virtuous effects of its two dimensional nature. For example, if-τ_(max) is 5% of the extend of the delay dimension and f_(d,max) is 5%of the Doppler dimension, the channel estimation subframe need only be5%×5%=0.25% of the OTFS frame.

Notice that although the channel estimation symbols are limited to asmall part of the OTFS frame, they actually sound the wholetime-frequency domain via the corresponding basis functions associatedwith these symbols.

A different approach to channel estimation is to devote pilot symbols ona subgrid in the time-frequency domain. This is akin to CRS pilots indownlink LTE subframes. One question in this approach is thedetermination of the density of pilots that is sufficient for channelestimation without introducing aliasing. Assume that the pilots occupythe subgrid (n₀T, m₀Δf) for some integers n₀, m₀. Recall that for thisgrid the SDFT will be periodic with period (1/n₀T, 1/m₀Δf). Then,applying the aliasing results discussed earlier to this grid, we obtainan alias free Nyquist channel support region of (±f_(d,max),±τ_(max))=(±1/2n₀T, ±1/2m₀Δf). The density of the pilots can then bedetermined from this relation given the maximum support of the channel.The pilot subgrid should extend to the whole time-frequency frame, sothat the resolution of the channel is not compromised.

OTFS-Access: Multiplexing More Than One User

There are many different ways to multiplex several uplink or downlinktransmissions in one OTFS frame. Some of the multiplexing methodsinclude:

(A) Multiplexing in the OTFS delay-Doppler domain

(B) Multiplexing in the time-frequency domain

(C) Multiplexing in the code spreading domain

(D) Multiplexing in the spatial domain

Multiplexing in the delay-Doppler domain: This may be the most naturalmultiplexing scheme for downlink transmissions. Different sets of OTFSbasis functions, or sets of information symbols or resource blocks aregiven to different users. Given the orthogonality of the basisfunctions, the users can be separated at the UE receiver. The UE needonly demodulate the portion of the OTFS frame that is assigned to it.

This approach could be made similar to the allocation of PRBs todifferent UEs in LTE. One difference is that, in OTFS, even a smallsubframe or resource block in the OTFS domain will be transmitted overthe whole time-frequency frame via the basis functions and willexperience the average channel response.

FIG. 15 illustrates this point by showing two different basis functionsbelonging to different users. Because of this, there is no compromise onchannel resolution for each user, regardless of the resource block orsubframe size.

In the uplink direction, transmissions from different users experiencedifferent channel responses. Hence, the different subframes in the OTFSdomain will experience a different convolution channel. This canpotentially introduce inter-user interference at the edges where twouser subframes are adjacent, and would require guard gaps to eliminateit. In order to avoid this overhead, a different multiplexing scheme canbe used in the uplink as explained next.

Multiplexing in the time-frequency domain: In this approach, resourceblocks or subframes are allocated to different users in thetime-frequency domain.

FIG. 16 illustrates this for a three user case. In this figure, User 1(blue) occupies the whole frame length but only half the availablesubcarriers. Users 2 and 3 (red and black respectively) occupy the otherhalf subcarriers, and divide the total length of the frame between them.

In this case, each user employs a slightly different version of the OTFSmodulation described above. One difference is that each user i performsan SFFT on a subframe (N_(i),M_(i)), N_(i)≤N,M_(i)≤M. This reduces theresolution of the channel, or in other words reduces the extent of thetime-frequency plane in which each user will experience its channelvariation. On the other side, this also gives the scheduler theopportunity to schedule users in parts of the time-frequency plane wheretheir channel is best.

To be able to extract the maximum diversity of the channel and allocateusers across the whole time-frequency frame, implementations canmultiplex users via interleaving. In this case, one user occupies asubsampled grid of the time-frequency frame, while another user occupiesanother subsampled grid adjacent to it.

FIG. 17 shows three users as before but interleaved on the subcarrierdimension. Of course, interleaving is possible in the time dimension aswell, and/or in both dimensions. The degree of interleaving, orsubsampling the grid per user is only limited by the spread of thechannel that we need to handle.

Multiplexing in the time-frequency spreading code domain: In embodimentsthat provide a random access PHY and MAC layer where users can accessthe network without having to undergo elaborate RACH (random accesschannel) and other synchronization procedures, e.g., to support Internetof Things (IoT) deployments, OTFS can support such a system by employinga spread-spectrum approach. Each user is assigned a differenttwo-dimensional window function that is designed as a randomizer. Thewindows of different users are designed to be nearly orthogonal to eachother and nearly orthogonal to time and frequency shifts. Each user thenonly transmits on one or a few basis functions and uses the window as ameans to randomize interference and provide processing gain. This canresult in a much simplified system that may be attractive for low cost,short burst type of IoT applications.

Multiplexing in the spatial domain: Similar to some other OFDMmulticarrier systems, a multi-antenna OTFS system can support multipleusers transmitting on the same basis functions across the wholetime-frequency frame. The users are separated by appropriate transmitterand receiver beamforming operations.

Additional examples of multi-user multiplexing are described withrespect to FIG. 21 to FIG. 27.

In some embodiments, the multiplexed signals include reference signalsand information signals targeted at UEs. The information signals mayinclude user data and/or other higher layer system information.Reference signals may include UE-specific reference signals, referencesignals intended by a logical group of UEs or reference signals to beused by all UEs being served by the transmitter.

As previously described, in some embodiments, both the reference signalsand the information signals, or signals carrying user data, aremultiplexed in the delay-Doppler domain and then transformed to thetime-frequency domain prior to transmission. In other words, in someembodiments, both reference signals for the system and informationsignals are carried in the transformed domain. As previously describedwith respect to FIG. 14, reference signals may be introduced usingminimal resources in the delay-Doppler domain, yet after OTFStransformation, may occupy the entire time-frequency range. Thus, highquality channel optimization is possible while using minimaltransmission resources.

In various embodiments, reference signals may be added to thetransmitted signals in the delay-Doppler domain and/or thetime-frequency domain, providing a system with a greater control of thelevel of optimization and transmission resource usage. In conventionalwireless systems, reference signals are often separated from user datasignals by leaving unused resources for practical implementations. UsingOTFS based transmission/reception techniques, due to the orthogonalityof basis functions and the application of a transform prior totransmitting reference function, such communication resources may nothave to be left unused—a receiver will be able to recover referencesignals that are relatively densely packed, e.g., without any unused ofblack space resources.

Implementation Issues

OTFS is a novel modulation technique with numerous benefits and a strongmathematical foundation. From an implementation standpoint, one addedbenefit is the compatibility with OFDM and the need for only incrementalchange in the transmitter and receiver architecture.

A typical OTFS implementation includes two steps. The Heisenbergtransform (which takes the time-frequency domain to the waveform domain)is typically already implemented in today's systems in the form ofOFDM/OFDMA. This implementation corresponds to a prototype filter g(t)which is a square pulse. Other filtered OFDM and filter bank variationshave been proposed for 5G, which can also be accommodated in thisgeneral framework with different choices of g(t).

The second step of OTFS is the two dimensional Fourier transform (SFFT).This can be thought of as a pre- and post-processing step at thetransmitter and receiver respectively as illustrated in FIG. 18. In thatsense it is similar, from an implementation standpoint, to the SC-FDMApre-processing step. As depicted in FIG. 18, processing from left toright, at the transmitter, QAM (or QPSK) symbols are input to an OTFSpreprocessing block which may then process the symbols as described inthis patent document. The output of the pre-processing block mayrepresent time-frequency samples, and then be input to a conventionalOFDM or filter bank based multicarrier transmission system. Theresulting signal is transmitted over a communication channel.

At the receiver-side, a conventional OFDM or filter bank demodulator maybe used to recover time-frequency domain samples. The time-frequencydomain samples may be input to the OTFS demodulation stage, shown as theOTFS post-processing and equalization stage in FIG. 18. In this stage,information bits and/or reference signals may be recovered using thevarious techniques described in the present document.

From a complexity comparison standpoint, for a frame of N OFDM symbolsof M subcarriers, SC-FDMA adds N DFTs of M point each (assuming worsecase M subcarriers given to a single user). The additional complexity ofSC-FDMA is then NMlog₂(M) over the baseline OFDM architecture. For OTFS,the 2D SFFT has complexity NMlog₂(NM)=NMlog₂(M)+NMlog₂(N), so the termNMlog₂(N) is the OTFS additional complexity compared to SC-FDMA. For anLTE subframe with M=1200 subcarriers and N=14 symbols, the additionalcomplexity is 37% more compared to the additional complexity of SC-FDMA.

In one advantageous aspect, from an architectural and implementationstandpoint, OTFS augments the PHY capabilities of an existing LTE modemarchitecture and does not introduce co-existence and compatibilityissues.

Examples of Benefits of OTFS Modulation

The OTFS modulation has numerous benefits that tie into the challengesthat 5G systems are trying to overcome. Arguably, the biggest benefitand the main reason to study this modulation is its ability tocommunicate over a channel that randomly fades within the time-frequencyframe and still provide a stationary, deterministic and non-fadingchannel interaction between the transmitter and the receiver. In theOTFS domain, all information symbols experience the same channel andsame SNR.

Further, OTFS best utilizes the fades and power fluctuations in thereceived signal to maximize capacity. To illustrate this point assumethat the channel consists of two reflectors which introduce peaks andvalleys in the channel response either across time or across frequencyor both. An OFDM system can theoretically address this problem byallocating power resources according to the waterfilling principle.However, due to practical difficulties such approaches are not pursuedin wireless OFDM systems, leading to wasteful parts of thetime-frequency frame having excess received energy, followed by otherparts with too low received energy. An OTFS system would resolve the tworeflectors and the receiver equalizer would employ coherent combining ofthe energy of the two reflectors, providing a non-fading channel withthe same SNR for each symbol. It therefore provides a channelinteraction that is designed to maximize capacity under the transmitassumption of equal power allocation across symbols (which is common inexisting wireless systems), using only standard AWGN codes.

In addition, OTFS provides a domain in which the channel can becharacterized in a very compact form. This has significant implicationsfor addressing the channel estimation bottlenecks that plague currentmulti-antenna systems and can be a key enabling technology foraddressing similar problems in future massive MIMO systems.

One benefit of OTFS is its ability to easily handle extreme Dopplerchannels. We have verified in the field 2×2 and 4×4, two and four streamMIMO transmission respectively in 90 Km/h moving vehicle setups. This isnot only useful in vehicle-to-vehicle, high speed train and other 5Gapplications that are Doppler intensive, but can also be an enablingtechnology for mm wave systems where Doppler effects will besignificantly amplified.

Further, OTFS provides a natural way to apply spreading codes anddeliver processing gain, and spread-spectrum based CDMA random access tomulticarrier systems. It eliminates the time and frequency fades commonto multicarrier systems and simplifies the receiver maximal ratiocombining subsystem. The processing gain can address the challenge ofdeep building penetration needed for IoT and PSTN replacementapplications, while the CDMA multiple access scheme can address thebattery life challenges and short burst efficiency needed for IOTdeployments.

Last but not least, the compact channel estimation process that OTFSprovides can be essential to the successful deployment of advancedtechnologies like Cooperative Multipoint (Co-MP) and distributedinterference mitigation or network MIMO.

It will be appreciated that the present document discloses OTFS, a novelmodulation scheme for wireless communications, with significantadvantages in performance, especially under significant Doppler effectsin mobility scenarios or mmWave communications. It will be appreciatedthat the present document discloses various attributes, compatibilityand design aspects and demonstrated the superiority of OTFS in a varietyof use cases.

FIG. 19 is a graphical representation of an OTFS delay-Dopplertransform. Graph 1902 depicts a two-dimensional plane with twoorthogonal axes—a delay axis and a Doppler axis, along whichtransmission resources are available as a grid of resources. Whentransformed through a 2D OTFS transform, as depicted by the stage 1904,the resulting signal may be represented in a second two-dimensionaltransmission resource plane using another two orthogonal axes, a timeaxis and a frequency axis. In the second two-dimensional resource plane,the signal may be identifiable similar to conventional LTE or othersystems which allocate transmission resources along time (slots) andfrequency (subcarrier). In addition, a coding or shaping window 1908 mayalso be used to multiplex (at a transmitter) and de-multiplex (at areceiver) signals.

FIG. 19 is a graphical representation of an OTFS delay-Doppler transformthat shows a single grid point transmission resource (T₀, v₀) spreadingacross the entire time-frequency plane after the 2D OTFS transformstage.

In FIGS. 21 to 27, several examples of multi-user multiplexing ofsignals are described.

FIG. 21 shows an example of a multi-user delay-Doppler transformmultiplexing. UE1 and UE2 each use a sparser lattice than the originallattice. Each UE uses every other point in the delay domain. The windowfor UE2 is shifted in frequency. It can be seen that both UEs have onlyhalf the resolution in the delay dimension. However, both UEs have fullresolution in the Doppler dimension and cover the entire span of delayand Doppler domains.

FIG. 22 shows an example of a multi-user delay-Doppler transformmultiplexing. This example shows multiplexing of three UEs: UE1, UE2 andUE3. Each UE uses a sparser lattice that the original lattice. UE1 usesevery other point in the delay domain. UE2 and UE3 use every other pointboth in the delay domain and the Doppler domain (v). UE2's window isshifted in the frequency domain and UE3's window is shifted both in thetime and frequency domains. As can be see, UE1 occupies half thedimension in the delay domain and full resolution in the Doppler domain.UE2 and UE3 have half the resolution in both delay and Doppler domainsand all three UEs cover the entire span of delay and Doppler domains.

FIG. 23 shows an example of a multi-user delay-Doppler transformmultiplexing. In this example, assignments to three UEs are shown. EachUE uses a sparser lattice than the original lattice. UE1 uses everyother point in the delay domain. UE2 and UE3 both use every other pointin both delay and Doppler domains. UE1 uses a split window. UE2 and UE3windows are shifted in frequency and UE3 window is also shifted in time.None of the lines above M/2 are copies of lines 0 to (M/4−1). Incomparison with the assignments discussed in FIG. 21, each user willexperience a different channel because the UEs are all using differentfrequencies.

FIG. 24 shows an example of a multi-user delay-Doppler transformmultiplexing. In this example, UE1 uses the left half of the originallattice and transmits zero power in the right half of the lattice.Conversely, UE2 uses the right half for transmission and sends zeropower in the left half (occupied by UE1's transmissions). The window ofUE1 and UE2 is contiguous on the full lattice. In this example, both UEshave full resolution in both delay and Doppler dimension, both UEs coverthe full Doppler span and each covers only half of the delay span. Inthe UL direction, a receiver may experience different channel conditionsdue to different UEs.

FIG. 25 shows an example of a multi-user delay-Doppler transformmultiplexing. In this example, UE1 uses the left half and sends zeropower in the right half of the original lattice. UE2 uses the rightbottom quarter and sends zero power in the remaining three-quarters ofthe original lattice. UE3 uses the right top quarter and sends zeropower in the remaining three-quarters of the original lattice. Thewindow of all three UEs is the same and is contiguous. Furthermore, allthree UEs have full resolution in both delay and Doppler domains. UE1covers the full Doppler span and half the delay span UE2 and UE3 coverhalf the delay span and half the Doppler span. In the uplink, a receiverwill experience different UL channels from different UEs.

FIG. 26 and FIG. 27 shows an example of a multi-user delay-Dopplertransform multiplexing in which UE1 and UE2 occupy overlapping resourcesin the Delay Doppler domain, but non-overlapping resources in thetime-frequency domain. Furthermore, each UE is assigned a separatefrequency range but occupies the entire time domain of the transmittedsymbol. In the delay-Doppler domain, the UEs occupy the same Tresources, but different v resources.

FIG. 28 shows a variation of the compatibility of OTFS scheme withconventional transmission schemes such as previously described withrespect to FIG. 18. Processing from left to right, at the transmitter,QAM (or QPSK) symbols are input to an OTFS preprocessing block which maythen process the symbols as described in this patent document. Theoutput of the pre-processing block may represent time-frequency samples,and then be input to a conventional OFDM or a universal filteredmulticarrier (UFMC) transmission system. The resulting signal istransmitted over a communication channel.

At the receiver-side, a conventional OFDM or UFMC demodulator may beused to recover time-frequency domain samples. The time-frequency domainsamples may be input to the OTFS demodulation stage, shown as the OTFSpost-processing and equalization stage in FIG. 28. In this stage,information bits and/or reference signals may be recovered using thevarious techniques described in the present document.

FIG. 29 shows a flowchart for an example signal transmission method2900. The method 2900 may be implemented at the transmitter-side. Forexample, in some embodiments, the method 2900 may be implemented at abase station in a cellular network such as a 5G network.

The method 2900 includes, at 2902, performing a logical mapping oftransmission resources of the digital communication channel along afirst two-dimensional resource plane represented by a first and a secondorthogonal axes corresponding to a first transmission dimension and asecond transmission dimension respectively. For example, the firsttwo-dimensional resource plan may be a delay-Doppler plane and the firstand the second orthogonal axes may correspond to the delay dimension andthe Doppler dimension.

The method 2900 includes, at 2904, allocating, to a first signal, afirst group of transmission resources from the logical mapping fortransmission.

The method 2900 includes, at 2906, transforming, using a firsttwo-dimensional transform, a combination of the first signal having thefirst group of transmission resources and the second signal having thesecond group of transmission resources to a corresponding transformedsignal in a second two-dimensional resource plane represented by a thirdand a fourth orthogonal axes corresponding to a third transmissiondimension and a fourth transmission dimension respectively. For example,the second two-dimensional resource plan may include a time-frequencyplane and the third orthogonal axis may correspond to the time dimensionand the fourth axis may correspond to the frequency dimension. In suchas case, transmission resources may correspond to time slots andsubcarriers.

The method 2900 includes, at 2908, converting the transformed signal toa formatted signal according to a transmission format of thecommunications channel.

The method 2900 includes, at 2910, transmitting the formatted signalover the communications channel. The formatted signal may be formattedaccording to a well-known format such as the LTE format or may comprisea 5G or another transmission protocol.

The method 2900 may operate such that transmission resources used by thefirst signal and the second signal are non-overlapping in at least oneof the first two-dimensional resource plane and the secondtwo-dimensional resource plane. FIG. 16, FIG. 17, FIGS. 21-27 andassociated description in this patent document describe some exampleembodiments of assignment of transmission resources to multiple signals.

As described with reference to FIG. 3, FIG. 4 and elsewhere, in someembodiments, the first two-dimensional resource plane comprises adelay-Doppler plane and the second two-dimensional resource planecomprises a time-frequency plane. This patent document also describesvarious multiplexing techniques in which the first signal and the secondsignal may each correspond to information signals intended for differentreceiving UEs, or may be pilot signals or reference signals, or acombination thereof.

In some embodiments, the transformed signal output from the firsttransform operation may be formatted by applying a multicarriermodulation scheme to the transformed signal. This patent documentdescribes several embodiments, including using OFDM modulation, FBMCmodulation, UFMC modulation, and so on. Furthermore, in someembodiments, the resulting signal may be produced to be compatible witha well-known or a legacy standard such as the LTE transmission format.In this way, in one advantageous aspect, the method 2900 may produce asignal that appears to be pre-processed and compatible with legacysystems for transmission.

In some embodiments, the resources allocated to the first and the secondsignal in the first two-dimensional resource plane may each use a set ofbasis function that are orthogonal to each other. Each set may includeone or more basis functions. FIG. 7 and FIG. 15 shows examples oforthogonal basis functions in the delay-Doppler domain that may be usedin the sets.

In various embodiments, to facilitate separation of the multiplexedversions of the first signal and the second signal, the non-overlappingresource utilization by the first signal and the second signal may be aproperty that is enforced both in the first two dimensional resourceplane and the second two-dimensional resource plane. Alternatively, thenon-overlapping nature of resource allocation may be only in thetime-frequency plane or in the delay-Doppler plane. It will beappreciated by one of skill in the art that, as long as the resourcesused are non-overlapping, and preferably orthogonal, receivers can usethis knowledge to recover the first signal and the second signalindividually.

In some embodiments, a first resource window and a second resourcewindow may be assigned to the first and second signals. Some examplesare described in the figures in the present document. These windows maybe assigned such that the windows may have the same shape (e.g.,rectangular) and may be obtained from each other along time and/orfrequency axis shifts.

In some embodiments, different antenna resources may be assigned to thefirst and the second signals, thereby achieving spatial diversity oftransmission resources.

It will be understood by one of skill in the art that additional signalscould be multiplexed along with the first signal and the second signalto provide multiple access to greater than two logical signals bymultiplexing in a manner in which non-overlapping resources areallocated at least in the delay-Doppler domain or in the time-frequencydomain or both. In some embodiments, the resource utilization in one ofthe two-dimensional resource plane may be partially or completelyoverlapping. FIG. 21 shows an example in which two UEs are allocatedoverlapping resources in the delay-Doppler domain but arenon-overlapping in the time-frequency domain.

In some embodiments, signal separation at the receiver-side isfacilitated by converting the transform signal to the formatted signalby code division multiplexing the transformed signal using atwo-dimensional code to generate a code-division multiplexed signal, andperforming a multicarrier modulation operation on the code-divisionmultiplexed signal. In some embodiments, randomization of phases may beperformed prior to the multicarrier modulation to achieve interferenceminimization and avoidance from neighboring cells. For example, therandomization may be a function of identify of the cell.

FIG. 30 is a flowchart depiction of a method 3000 of single reception.The method 3000 may be implemented by a receiver apparatus such as abase station or UE.

The method 3000 includes receiving (3002) a signal transmissioncomprising at least two component signals multiplexed together.

The method 3000 includes transforming (3004), using an orthogonaltransform, the signal transmission into a post-processing format,wherein the post-processing format represents the at least two componentsignals in a two-dimensional time-frequency plane.

The method 3000 includes recovering (3006), by performing an orthogonaltime frequency space transformation, a multiplexed signal in atwo-dimensional delay-Doppler plane, from the post-processing format.

The method 3000 includes demultiplexing (3008) the multiplexed signal torecover one of the at least two component signals.

In various embodiments, the method 3000 may operate such that a receivermay be able to successfully receive and recover the first signal and thesecond signal transmitted according to the method 2900.

In some embodiments, a signal transmission method may include performinga logical mapping of transmission resources of the digital communicationchannel along a first two-dimensional resource plane represented by afirst and a second orthogonal axes corresponding to a first transmissiondimension and a second transmission dimension respectively, allocating,to a first signal, a first group of transmission resources from thelogical mapping for transmission, transforming, using a firsttwo-dimensional transform, the first signal having the first group oftransmission resources to a corresponding transformed signal in a secondtwo-dimensional resource plane represented by a third and a fourthorthogonal axes corresponding to a third transmission dimension and afourth transmission dimension respectively, converting the transformedsignal to a formatted signal according to a transmission format of thecommunications channel, and transmitting the formatted signal over thecommunications channel.

In some embodiments, a receiver apparatus may receive the single signaltransmitted according to the above-described method and successfullyreceive the signal by processing through two transforms—a firsttransform that enables processing in the time-frequency resource plane,followed by a second transform that enables processing and symbolrecovery in the delay-Doppler plane.

Proof of Proposition 1: Let

g ₁(t)=∫∫h ₁(τ,v)e ^(j2πv(t−τ)) g(t−τ)dvdτ  (57)

g ₂(t)=∫∫h ₂(τ,v)e ^(j2πv(t−τ)) g ₁(t−τ)dvdτ  (58)

Substituting (58) into (57) we obtain after some manipulation

g ₂(t)=∫∫f(τ,v)e ^(j2πv(t−τ)) g(t−τ)dvdτ  (59)

with f(τ,v) given by (16).

Proof of Theorem 1: The theorem can be proven by straightforward buttedious substitution of the left hand side of (23); by definition

$\begin{matrix}{{\left. {{{A_{g_{r},{\Pi_{f}{(g_{tr})}}}\left( {\tau,v} \right)} = {< {{g_{r}\left( {t - \tau} \right)}e^{j2\pi vt}}}},{{\Pi_{f}\left( g_{tr} \right)}>={\int{{g_{r}^{*}\left( {t - \tau} \right)}e^{{- j}\; 2\;{vt}}{\Pi_{f}\left( g_{tr} \right)}(t)}}}} \right){dt}} = {\int{{g_{r}^{*}\left( {t - \tau} \right)}e^{{- j}2\pi vt}{\int{\int{{f\left( {\tau^{\prime},v^{\prime}} \right)}e^{{j2\pi v}^{\prime}{({t - \tau^{\prime}})}}{g_{tr}\left( {t - \tau^{\prime}} \right)}{dv}^{\prime}{d\tau}^{\prime}{dt}}}}}}} & (60)\end{matrix}$

By changing the order of integration and the variable of integration(t−τ′)→t we obtain

$\begin{matrix}{{A_{g_{r},{\Pi_{f}{(g_{tr})}}}\left( {\tau,v} \right)} = {{\int{\int{{f\left( {\tau^{\prime},v^{\prime}} \right)}e^{{j2\pi v}^{\prime}{({t - \tau^{\prime}})}}{\int{{g_{r}^{*}\left( {t - \tau} \right)}{g_{tr}\left( {t - \tau^{\prime}} \right)}e^{{- j}2\pi vt}{dtdv}^{\prime}{d\tau}^{\prime}}}}}} = {\int{\int{{f\left( {\tau^{\prime},v^{\prime}} \right)}e^{j\; 2\;{v^{\prime}{({\tau - \tau^{\prime}})}}}{A_{g_{r},g_{tr}}\left( {{\tau - \tau^{\prime}},{v - v^{\prime}}} \right)}e^{{j2\pi v}^{\prime}{({\tau - \tau^{\prime}})}}{dv}^{\prime}{d\tau}^{\prime}}}}}} & (61)\end{matrix}$

where

A _(g) _(r) _(,g) _(tr) (τ−τ′,v−v′)=∫g _(r)*(t−(τ−τ′))g _(tr)(t)e^(−j2π(v−v′)t−(τ−τ′)) dt   (62)

Notice that the right second line of (61) is exactly the right hand sideof (23), which is what we wanted to prove.□

Proof of Theorem 2: Substituting into (23) and evaluating on the latticeA we obtain:

$\begin{matrix}{{\hat{X}\left\lbrack {m,n} \right\rbrack} = {{\sum\limits_{m^{\prime} = {- \frac{M}{2}}}^{\frac{M}{2} - 1}\;{\sum\limits_{n^{\prime} = 0}^{N - 1}\;{{X\left\lbrack {m^{\prime},n^{\prime}} \right\rbrack} \times {\int{\int{{h\left( {{\tau - {nT}},{v - {m\Delta f}}} \right)}{A_{g_{r},g_{tr}}\left( {{{nT} - \tau},{{m\Delta f} - v}} \right)}e^{j\; 1\;{v{({{nT} - \tau})}}t}}}}}}} + {V\left\lbrack {m,n} \right\rbrack}}} & (63)\end{matrix}$

Using the bi-orthogonality condition in (63) only one term survives inthe right hand side and we obtain the desired result of (29).

Proof of Proposition 2: Based on the definition of SFFT, it is not hardto verify that a delay translates into a linear phase

$\begin{matrix}{{{SFFT}\left( {X_{2}\left\lbrack {{n - k},{m - l}} \right\rbrack} \right)} = {SFF{T\left( {X_{2}\left\lbrack {n,m} \right\rbrack} \right)}e^{{- j}2{\pi{({\frac{nk}{N} - \frac{ml}{M}})}}}}} & (64)\end{matrix}$

Based on this result we can evaluate the SFFT of a circular convolution

$\begin{matrix}{{{SFFT}\left( {\sum\limits_{k = 0}^{N - 1}{\sum\limits_{l = {- \frac{M}{2}}}^{\frac{M}{2} - 1}{{X_{1}\left\lbrack {k,l} \right\rbrack}{X_{2}\left\lbrack {{\left( {n - k} \right)\ {mod}\ N},{\left( {- l} \right)\ {mod}\ M}} \right\rbrack}}}} \right)} = {{\sum\limits_{k = 0}^{N - 1}\;{\sum\limits_{\;_{l = {- \frac{M}{2}}}}^{\frac{M}{2} - 1}{{X_{1}\left\lbrack {k,l} \right\rbrack}{{SFFT}\left( {X_{2}\left\lbrack {n,m} \right\rbrack} \right)}e^{{- j}2{\pi{({\frac{nk}{N} - \frac{ml}{M}})}}}}}} = {{SFF}{T\left( {X_{1}\left\lbrack {n,m} \right\rbrack} \right)}SFF{T\left( {X_{2}\left\lbrack {n,m} \right\rbrack} \right)}}}} & (65)\end{matrix}$

yielding the desired result.

Proof of Proposition 3: We have already proven that on thetime-frequency domain we have a multiplicative frequency selectivechannel given by (29). This result, combined with the interchange ofconvolution and multiplication property of the symplectic Fouriertransform [c.f. Proposition 1 and Eq. (42)] leads to the desired result.

In particular, if we substitute Y(n,m) in the demodulation equation (48)from the time-frequency channel equation (29) and X[n,m] in (29) fromthe modulation equation (43) we get a (complicated) end-to-endexpression

$\begin{matrix}{{\hat{x}\left\lbrack {k,l} \right\rbrack} = {\frac{1}{MN}{\sum\limits_{k^{\prime} = 0}^{N - 1}\;{\sum\limits_{l^{\prime} = 0}^{M - 1}\;{{x\left\lbrack {k^{\prime},l^{\prime}} \right\rbrack}{\int{\int{{h\left( {\tau,v} \right)}e^{{- j}\; 2{v\tau}} \times \times {\quad{\left\lbrack {\sum\limits_{m = 0}^{L - 1}\;{\sum\limits_{n = 0}^{K - 1}\;{{W\left( {n,m} \right)}e^{{- j}\; 2\;{{nT}{({\frac{k - k^{\prime}}{NT} - v})}}}e^{j\; 2\;{{m\Delta f}{({\frac{l - l^{\prime}}{M\Delta f} - \tau})}}}}}} \right\rbrack{dvd\tau}}}}}}}}}}} & (66)\end{matrix}$

Recognizing the factor in brackets as the discrete symplectic Fouriertransform of W(n,m) we have

$\begin{matrix}{{\overset{\hat{}}{x}\left\lbrack {k,l} \right\rbrack} = {\frac{1}{MN}{\sum\limits_{k^{\prime} = 0}^{N - 1}{\sum\limits_{l^{\prime} = 0}^{M - 1}{{x\left\lbrack {k^{\prime},l^{\prime}} \right\rbrack}{\int{\int{{h\left( {\tau,v} \right)}e^{{- j}2\pi v\tau}{w\left( {{\frac{l - l^{\prime}}{M\Delta f} - \tau},{\frac{k - k^{\prime}}{NT} - v}} \right)}dvd\tau}}}}}}}} & (67)\end{matrix}$

Further recognizing the double integral as a convolution of the channelimpulse response (multiplied by an exponential) with the transformedwindow we obtain

$\begin{matrix}{{\overset{\hat{}}{x}\left\lbrack {k,l} \right\rbrack} = {\frac{1}{MN}{\sum\limits_{k^{\prime} = 0}^{N - 1}{\sum\limits_{l^{\prime} = 0}^{M - 1}{{x\left\lbrack {k^{\prime},l^{\prime}} \right\rbrack}{h_{w}\left( {{\frac{l - l^{\prime}}{M\Delta f} - \tau},{\frac{k - k^{\prime}}{NT} - v}} \right)}}}}}} & (68)\end{matrix}$

which is the desired result.

It will be appreciated that various techniques are disclosed fortransmitting and receiving data using OTFS modulation techniques.

FIG. 31 shows an example of a wireless transceiver apparatus 3100. Theapparatus 3100 may be used to implement method 2900 or 3000. Theapparatus 3100 includes a processor 3102, a memory 3104 that storesprocessor-executable instructions and data during computations performedby the processor. The apparatus 3100 includes reception and/ortransmission circuitry 3106, e.g., including radio frequency operationsfor receiving or transmitting signals.

The disclosed and other embodiments and the functional operationsdescribed in this document can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this document and their structural equivalents,or in combinations of one or more of them. The disclosed and otherembodiments can be implemented as one or more computer program products,i.e., one or more modules of computer program instructions encoded on acomputer readable medium for execution by, or to control the operationof, data processing apparatus. The computer readable medium can be amachine-readable storage device, a machine-readable storage substrate, amemory device, a composition of matter effecting a machine-readablepropagated signal, or a combination of one or more them. The term “dataprocessing apparatus” encompasses all apparatus, devices, and machinesfor processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. Theapparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them. A propagated signal is an artificially generated signal, e.g.,a machine-generated electrical, optical, or electromagnetic signal, thatis generated to encode information for transmission to suitable receiverapparatus.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a stand-alone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this document can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andany one or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Computer readable media suitable for storingcomputer program instructions and data include all forms of non volatilememory, media and memory devices, including by way of examplesemiconductor memory devices, e.g., EPROM, EEPROM, and flash memorydevices; magnetic disks, e.g., internal hard disks or removable disks;magneto optical disks; and CD ROM and DVD-ROM disks. The processor andthe memory can be supplemented by, or incorporated in, special purposelogic circuitry.

While this document contains many specifics, these should not beconstrued as limitations on the scope of an invention that is claimed orof what may be claimed, but rather as descriptions of features specificto particular embodiments. Certain features that are described in thisdocument in the context of separate embodiments can also be implementedin combination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesub-combination. Moreover, although features may be described above asacting in certain combinations and even initially claimed as such, oneor more features from a claimed combination can in some cases be excisedfrom the combination, and the claimed combination may be directed to asub-combination or a variation of a sub-combination. Similarly, whileoperations are depicted in the drawings in a particular order, thisshould not be understood as requiring that such operations be performedin the particular order shown or in sequential order, or that allillustrated operations be performed, to achieve desirable results.

Only a few examples and implementations are disclosed. Variations,modifications, and enhancements to the described examples andimplementations and other implementations can be made based on what isdisclosed.

1. A signal reception method, implemented at a receiver apparatus, comprising: receiving a signal transmission comprising at least two component signals multiplexed together; transforming, using an orthogonal transform, the signal transmission into a post-processing format, wherein the post-processing format represents the at least two component signals in a two-dimensional time-frequency plane; recovering, by performing an orthogonal time frequency space transformation, a multiplexed signal in a two-dimensional delay-Doppler plane, from the post-processing format; and demultiplexing the multiplexed signal to recover one of the at least two component signals.
 2. The method of claim 1, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal carrying user data for another receiver apparatus.
 3. The method of claim 1, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal comprising a reference signal transmission.
 4. The method of claim 1, wherein the at least two component signals are multiplexed together to occupy non-overlapping transmission resources in the two-dimensional time-frequency plane.
 5. The method of claim 1, wherein the at least two component signals are multiplexed together to occupy non-overlapping transmission resources in the two-dimensional delay-Doppler plane.
 6. The method of claim 1, wherein the at least two component signals are composed of mutually orthogonal basis functions in the delay-Doppler plane.
 7. The method of claim 1, wherein the orthogonal transform comprises a Wigner transform.
 8. A receiver apparatus, comprising: a transceiver configured to receive a signal transmission comprising at least two component signals multiplexed together; and a processor, coupled to the transceiver, configured to: transform, using an orthogonal transform, the signal transmission into a post-processing format, wherein the post-processing format represents the at least two component signals in a two-dimensional time-frequency plane, recover, by performing an orthogonal time frequency space transformation, a multiplexed signal in a two-dimensional delay-Doppler plane, from the post-processing format, and demultiplex the multiplexed signal to recover one of the at least two component signals.
 9. The receiver apparatus of claim 8, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal carrying user data for another receiver apparatus.
 10. The receiver apparatus of claim 8, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal comprising a reference signal transmission.
 11. The receiver apparatus of claim 8, wherein the at least two component signals are multiplexed together to occupy non-overlapping transmission resources in the two-dimensional time-frequency plane.
 12. The receiver apparatus of claim 8, wherein the at least two component signals are multiplexed together to occupy non-overlapping transmission resources in the two-dimensional delay-Doppler plane.
 13. The receiver apparatus of claim 8, wherein the at least two component signals are composed of mutually orthogonal basis functions in the delay-Doppler plane.
 14. The receiver apparatus of claim 8, wherein the orthogonal transform comprises a Wigner transform.
 15. A non-transitory computer-readable storage medium having instructions stored thereupon for signal reception, comprising: instructions for receiving a signal transmission comprising at least two component signals multiplexed together; instructions for transforming, using an orthogonal transform, the signal transmission into a post-processing format, wherein the post-processing format represents the at least two component signals in a two-dimensional time-frequency plane; instructions for recovering, by performing an orthogonal time frequency space transformation, a multiplexed signal in a two-dimensional delay-Doppler plane, from the post-processing format; and instructions for demultiplexing the multiplexed signal to recover one of the at least two component signals.
 16. The method of claim 15, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal carrying user data for another receiver apparatus.
 17. The method of claim 15, wherein the at least two component signals include a first component signal carrying user data for the receiver apparatus and a second component signal comprising a reference signal transmission.
 18. The method of claim 15, wherein the at least two component signals are multiplexed together to occupy non-overlapping transmission resources in the two-dimensional time-frequency plane or in the two-dimensional delay-Doppler plane.
 19. The method of claim 15, wherein the at least two component signals are composed of mutually orthogonal basis functions in the delay-Doppler plane.
 20. The method of claim 15, wherein the orthogonal transform comprises a Wigner transform. 